How to Use this Volume

Transcription

How to Use this Volume
This volume is divided into the following sections (see index in the following page; click
on the corresponding section name to go to the section):
• Program. Full description of the program including, in chronological order, the
information about the sessions: Name, 4 character code, room, chairperson and
titles and authors of each communication. Each communication is linked to the
corresponding abstract in the Abstracts section.
• Abstracts. Contains the abstracts of all the communications presented at the conference, alphabetically ordered by titles. Each abstract contains the title, authors
and affiliations, abstract and keywords.
• List of keywords. All the keywords used in the different abstracts are listed in
alphabetical order. For each keyword, we show the pages of the abstracts where
this keywords appear. Each page is a link to the corresponding abstract.
• List of authors. All authors and coauthors of communications are listed in alphabetical order. For each abstract authored by a person, we display the 4 character code
of the corresponding session and the page of the corresponding abstract (linked to
the abstract in the Abstracts section). The code of the session appears in boldface
if the author is presenting the communication.
1
Contents
Program
2
Abstracts
45
List of Keywords . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378
List of Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387
1
Program
Monday - 17th June
Opening Session
Room A (Paraninfo) - 9:15
Session MO1A (plenary)
Plenary 1
Chaired by Stan Zionts
1. Creating, Structuring, and Using Multiple Objectives [10:00 - 11:00]
by Ralph Keeney (*).
Session MO2B (contributed)
Reference Point Methods
Chaired by Willem K. M. Brauers
Room B (Milton Friedman) - 11:20
1. On the Optimal Inputs Balance in Terms of the Educational Multiobjective Model:
the Case of Primary and Secondary Education Students [11:20 - 11:45]
´
by Mariano Luque (*), Luis Alejandro López Agudo, Oscar
Marcenaro-Gutiérrez.
2. Synhronous Usage of Parameterized Achievement Scalarizing Functions in Interactive Compromise Programming [11:45 - 12:10]
by Yury Nikulin (*), Volha Karelkina.
3. Goal Programming Model for Management Accounting: A New Typology [12:10 12:35]
by Sheila McGillis (*), Belaid Aouni.
4. Optimization of Investment in Shares by Multi-Objective Optimization [12:35 13:00]
by Willem K. M. Brauers (*).
2
Session MO2C (contributed)
Room C (Paul A. Samuelson) - 11:20
Interactive Multi-Objective Optimization (I)
Chaired by Murat Koksalan
1. An Interactive Algorithm for Multiobjective Mixed Integer Programming Problems
[11:20 - 11:45]
by Ozgur Ozpeynirci (*), Murat Koksalan, Banu Lokman.
2. Solving a Mutiobjective Dynamic Problem with a Stochastic Transition Function
and Stochastic Objective Function by Interactive Procedure [11:45 - 12:10]
by Maciej Nowak (*), Tadeusz Trzaskalik.
3. An Enumerative Cutting Plane Approach to Integer Linear Vector Optimization
Problems [12:10 - 12:35]
by Walter Habenicht (*).
4. An Interactive Algorithm for Multi-objective Integer Programs [12:35 - 13:00]
by Murat Koksalan (*), Pekka Korhonen, Banu Lokman, Jyrki Wallenius.
Session MO2D (contributed)
Room D (Franco Modigliani) - 11:20
Environmental Multi-Criteria Decision Making (I)
Chaired by Judit Lienert
1. MCDM and GIS to Identify Land Suitability for Agriculture [11:20 - 11:45]
by Mendas Abdelkader (*).
2. Problems, Objectives and Ecosystem Services in Protected Areas: A Mediterranean
Case Study [11:45 - 12:10]
by Marina Segura (*), Mónica de Castro Pardo, Concepcion Ginestar, Concepción
Maroto.
3. Constructing Ecological Value Functions for River Rehabilitation: Expert Elicitation with Different Aggregation Schemes [12:10 - 12:35]
by Judit Lienert (*), Simone Langhans, Peter Reichert.
4. Abstract REMOVED [12:35 - 13:00]
3
Session MO2E (contributed)
Goal Programming
Chaired by Belaid Aouni
Room E (James M. Buchanan) - 11:20
1. Goal Programming Approach to Workforce Scheduling Problem for a Restaurant
[11:20 - 11:45]
by G¨
ul¸cin C
¸ öl (*), Servet Hasg¨
ul.
2. Error Correction Model in Classification by Using Multiple-Criteria and MultipleConstraint Levels Linear Programming [11:45 - 12:10]
by Bo Wang (*), Yong Shi, Wei Xianhua.
3. Augmented Goal Programming with Multi Fuzzy Targets and Coefficients [12:10 12:35]
by Hocine Mouslim (*), Mustapha Belmokaddem, Sakina Melloul.
4. Some Epistemological Considerations of the Goal Programming Model [12:35 13:00]
by Belaid Aouni (*).
Session MO2F (invited)
Room F (Lawrence R. Klein) - 11:20
Evolutionary Multiobjective Optimization (EMO-I)
Chaired by Dimo Brockhoff
1. Multi-Objective Reinforcement Learning using Sets of Pareto Dominating Policies
[11:20 - 11:45]
by Kristof Van Moffaert (*), Madalina M. Drugan, Ann Nowé.
2. Adaptive Guided Evolutionary Multi-Objective Optimization [11:45 - 12:10]
by Florian Siegmund (*), Kalyanmoy Deb, Amos Ng.
3. FEMOEA: a Fast and Efficient Multi-Objective Evolutionary Algorithm [12:10 12:35]
by Juana López-Redondo (*), José Fernández-Hernández, Pilar Mart´ınez-Ortigosa.
4. Dynamic Resampling for Guided Evolutionary Multi-Objective Optimization of
Stochastic Systems [12:35 - 13:00]
by Florian Siegmund (*), Kalyanmoy Deb, Alexander Karlsson, Amos Ng.
4
Session MO2G (invited)
Room G (Robert M. Solow) - 11:20
Socially Responsible Investment Decision Making (I)
Chaired by Blanca Pérez-Gladish
1. A Multi-dimensional Corporate Social Performance Versus a Multi-dimensional Corporate Social Irresponsibility [11:20 - 11:45]
by Homayoon Shalchian (*), Kais Bouslah, Khalid El badraoui, Jean-Jacques Lilti,
Bouchra M’Zali.
2. Markowitz’s Investment Problem under Multicriteria, Uncertainty and Risk [11:45
- 12:10]
by Vladimir Korotkov (*), Vladimir Emelichev.
3. Tri-criterion Inverse Portfolio Optimization with Application to Socially Responsible
Mutual Funds [12:10 - 12:35]
by Maximilian Wimmer (*), Markus Hirschberger, Ralph E. Steuer, Sebastian Utz.
4. Synthetic Indicators of Mutual Funds’ Environmental Responsibility: An application of the Reference Point Method [12:35 - 13:00]
by Blanca Pérez-Gladish (*), Jose Manuel Cabello, Paz Méndez Rodr´ıguez, Francisco Ruiz.
Session MO3B (contributed)
AHP/ANP: Methodological Issues (I)
Chaired by Ludmil Mikhailov
1. Multi-Criteria Decision Making Applications in Higher Open and Distance Learning
Systems [14:30 - 14:55]
by Zehra Kamisli Ozturk (*).
2. A Decision Model for Identifying and Prioritizing the Capability Gaps in Defense
Planning [14:55 - 15:20]
by Ahmet Kandakoglu (*), Y.Riza Kahraman, Ilker Topcu.
3. Microsoft Excel as a Tool for Solving Multicriteria Decision Problems [15:20 - 15:45]
by Radomir Perzina (*), Jaroslav Ramik.
4. Multiobjective Evolutionary Approach to Preference Elicitation in the Analytic Hierarchy Process [15:45 - 16:10]
by Ludmil Mikhailov (*).
5
Session MO3C (contributed)
Multi-Attribute Utility Theory (I)
Chaired by Hannele Wallenius
1. Grey Numbers and Multi-Criteria Decision Making [14:30 - 14:55]
by Alan Pearman (*), Naiming Xie.
2. Multicriteria Cognitive Map: an Integrated Tool for Building a Multicriteria Evaluation Model [14:55 - 15:20]
by Teresa Cipriano Rodrigues (*), Mónica Duarte Oliveira.
3. Development of Interactive Support System Using Spreadsheet based on Multiattribute Utility Analysis [15:20 - 15:45]
by Hayashida Tomohiro (*), Nishizaki Ichiro, Imai Mitsuhiro.
4. Multi-Attribute Online Reverse Auctions [15:45 - 16:10]
by Hannele Wallenius (*), Pham Long, Jeffrey Teich, Jyrki Wallenius.
Session MO3D (contributed)
Agricultural & Forestry Resources Management
Chaired by Carlos Romero
1. A Model for a Spatial Forest Problem Taking into Account Economic and Environmental Objectives [14:30 - 14:55]
by Monica Hernandez (*), Rafael Caballero Fernández, Trinidad Gómez, M. Amparo
Leon, Julián Molina Luque.
2. Quantifying Public Preferences for Evaluation Multifunctional Agriculture System,
using Analytic Hierarchy Process [14:55 - 15:20]
by Inmaculada Marqués (*), José Lu´ıs Pérez-Salas, Baldomero Segura Garc´ıa del
R´ıo.
3. Hydrological Constraints in Optimal Timber Harvest Scheduling Problems: A Case
Study in Eucalyptus Plantations [15:20 - 15:45]
by Juan Carlos Giménez (*), Mercedes Bertomeu Garc´ıa, Luis D´ıaz-Balteiro.
4. Multiple Criteria Methods in Decision Support Systems for Forest Management
[15:45 - 16:10]
by Marina Segura (*), Concepción Maroto, Duncan Ray.
6
Session MO3E (contributed)
Advancements in MCDM Theory
Chaired by Ralph E. Steuer
1. Biases and Path Dependency in the Even Swaps Method [14:30 - 14:55]
by Raimo Hämäläinen (*), Tuomas Lahtinen.
2. NIMBUS in Interactive Multiobjective Optimization under Uncertainty [14:55 15:20]
by Kaisa Miettinen (*), Jyri Mustajoki, Theodor Stewart.
3. Evidential Reasoning Rule for Criteria Combination in MCDM [15:20 - 15:45]
by Ling Xu (*), Jian-Bo Yang.
4. A Careful Look at the Importance of Criteria and Weights [15:45 - 16:10]
¨ orni, Kari Silvennoinen.
by Jyrki Wallenius (*), Pekka Korhonen, Anssi O¨
Session MO3F (contributed)
Preference Based EMO
Chaired by Ignacy Kaliszewski
1. The Global WASF-GA: An Evolutionary Algorithm in Multiobjective Optimization
to Approximate the whole Pareto Optimal Front [14:30 - 14:55]
by Ana Belén Ruiz (*), Mariano Luque, Rubén Saborido-Infantes.
2. Theoretically Analyzing Simple Interactive Evolutionary Multiobjective Optimizers
[14:55 - 15:20]
by Dimo Brockhoff (*).
3. Optimization of the Operation of the Auxiliary Services of Power Plants using
Preference-based Evolutionary Optimization [15:20 - 15:45]
by Ana Belén Ruiz (*), Jose Manuel Cabello, Mariano Luque, Francisco Ruiz.
4. Framing Mechanical Design as Multiple Criteria Decision Making Processes [15:45
- 16:10]
by Ignacy Kaliszewski (*), Tomasz Kiczkowiak, Janusz Miroforidis.
7
Session MO3G (invited)
Value-Focused Thinking
Chaired by Johannes Siebert
1. Creating a Balanced Scorecard by using Value-Focused Thinking [14:30 - 14:55]
by Joschka Muetterlein (*), Reinhard Kunz, Johannes Siebert.
2. Facing the Energy Transition: An Illustration how Value-Focused Thinking helps
to Find the Right Answers [14:55 - 15:20]
by Peter Krampf (*), Johannes Siebert.
3. Generating an Optimal Set of Alternatives using Value-Focused Thinking [15:20 15:45]
by Johannes Siebert (*).
4. Concept and Empirical Verification of the Benefit of Value-Focused Thinking [15:45
- 16:10]
by Reinhard Kunz (*), Johannes Siebert.
Session MO4B (invited)
New Methods for Multi-Objective Optimization (I)
Chaired by Kaisa Miettinen
1. An Interactive Algorithm for Bi-objective Routing Problems [16:30 - 16:55]
by Diclehan Tezcaner (*), Murat Koksalan.
2. Multi-Objective Modelling and Simulation in New Product Development (NPD)
using the Belief Rule-Based (BRB) Methodology [16:55 - 17:20]
by Emanuel Savan (*), Yu-Wang Chen, Ling Xu, Jian-Bo Yang.
3. A Conic Scalarization Method in Multi-Objective Optimization [17:20 - 17:45]
by Refail Kasimbeyli (*).
4. Nonconvex Pareto Navigator for Interactive Multiobjective Optimization [17:45 18:10]
by Markus Hartikainen (*), Kathrin Klamroth, Kaisa Miettinen.
8
Session MO4C (invited)
Approximate Solutions and Optimality Conditions in Vector Optimization
Chaired by Vicente Novo
1. Approximations of Set-Valued Optimization Problems [16:30 - 16:55]
by Rubén López Montoya (*), Elvira Hernández.
2. Efficient Solutions for Non-Regular Multiobjective Problems [16:55 - 17:20]
by Mar´ıa Beatriz Hernández Jiménez (*), Rafaela Osuna-Gómez, Gabriel RuizGarzón.
3. Variational-Like Inequality Problems as an Important Tool to Solve Vectorial Optimization Problems [17:20 - 17:45]
by Gabriel Ruiz-Garzón (*), Lucelina Batista dos Santos, Rafaela Osuna-Gómez,
Antonio Rufián-Lizana.
4. Characterization and Properties of Approximate Proper Solutions in Vector Optimization Problems [17:45 - 18:10]
by Lidia Huerga (*), César Gutiérrez, Bienvenido Jiménez, Vicente Novo.
Session MO4D (invited)
Methods with Guaranteed Performance for Multi-Objective Combinatorial Optimization (I)
Chaired by Xavier Gandibleux
1. A Parameterized Approximation for Matroids with Multiple Objectives [16:30 16:55]
by Lydia Tlilane (*), Laurent Gourvès, Jérôme Monnot.
2. A Primal-Dual Approach for Optimizing Ordered Weighted Average in Perfect
Matching Problems [16:55 - 17:20]
by Paul Weng (*), Viet Hung Nguyen.
3. Approximations for the Multi-objective Spanning Tree Problem [17:20 - 17:45]
by Anisse Ismaili (*).
4. Surrogate-Based Algorithm for Computing an Upper Bound set for the 0/1 BiObjective Bi-Dimensional Knapsack Problem [17:45 - 18:10]
by Audrey Cerqueus (*), Xavier Gandibleux, Anthony Przybylski.
9
Session MO4E (Wiley Prize)
Wiley Practice Prize Competition
Chaired by Theodor Stewart
1. Application of an Intelligent Decision System to Assess Students’ Academic Performance in University Early Admission [16:30 - 16:55]
by Yu-Wang Chen (*), Yue Chen, Ling Xu, Jian-Bo Yang.
2. Triple Bottomline, Hyper-radial-Visualisation-Based ’Decision-Making by Shopping’
for a Land Use Management Problem using Evolutionary Multi-objective Optimisation [16:55 - 17:20]
by Oliver Chikumbo (*), Kalyanmoy Deb, Erik Goodman.
3. Integrating Qualitative Assessment and Fuzzy AHP to Prioritize Investments in an
Energy Efficiency Program of Favelas in the City of Rio de Janeiro [17:20 - 17:45]
by Jose Roberto Ribas (*), Mariana Rocha, Juliana Ribas Severo.
4. Multi-Objective Dynamic Scheduling Optimization Model for Real-Time Rebalancing Control of the Hybrid Assembly Lines [17:45 - 18:10]
by Wai Keung Wong (*).
Session MO4F (invited)
Multi-Criteria Models applied to Sustainable Environment and Efficient
Use of Energy
Chaired by Carlos Enrique Escobar-Toledo
1. Multicriteria Models Applied to Sustainable Enviroment and Efficient Use of Energy
[16:30 - 16:55]
by Lol-chen Alegr´ıa Mej´ıa (*), Carlos Enrique Escobar-Toledo.
2. Elicitation Procedures for the Comparison of Decisional Maps [16:55 - 17:20]
by Valerie Brison (*), Marc Pirlot.
3. A Stakeholder based Multi-Criteria Evaluation Framework for City Distribution
[17:20 - 17:45]
by Lauriane Milan (*), Cathy Macharis, Sara Verlinde.
4. Multi-Criteria Decision Making: Exergy and Energy Life Cycle Analyses for Plastic
Individual Disposable Bags versus other Material [17:45 - 18:10]
by Carlos Enrique Escobar-Toledo (*), Lol-chen Alegr´ıa Mej´ıa, Bárbara Ram´ırez.
10
Session MO4G (contributed)
Risk MCDM Modeling (I)
Chaired by Rafael Sarkisyan
1. Multi-Criteria Decision-Making Perspective on the Privacy versus Security Conundrum [16:30 - 16:55]
by Sonia Toubaline (*), Hervé Borrion, Tanya Le Sage, Timothy Mitchener-Nissen.
2. Enhancing Portfolio Risk Analysis - Balancing the Past, the Future, the Objective,
the Subjective [16:55 - 17:20]
by Chris Myers (*).
3. Adversarial Risk Analysis on Transport Infrastructures [17:20 - 17:45]
by Javier Cano (*), David R´ıos Insua, Alessandra Tedeschi, Ugur Turhan.
4. Sensitivity and Efficiency of Multiple Criteria Alternatives of the Model ’Revenue Risk’ [17:45 - 18:10]
by Rafael Sarkisyan (*), Olga Eskova.
Session MO5E (PhD Awards)
MCDM Doctoral Dissertation Award
Chaired by Francisco Ruiz
1. Advanced Metaheuristics for Multi-objective Optimization: Design, Analysis and
Application [18:10 - 18:15]
by Juan J. Durillo (*).
2. New Directions in Robustness Analysis and Preference Modeling in Multiple Criteria
Decision Aiding [18:15 - 18:20]
by Milosz Kadzinski (*).
3. Hybrid Evolutionary Multi-Objective Optimization for Enhanced Convergence and
Diversity [18:20 - 18:25]
by Karthik Sindhya (*).
11
Session MO5H (poster)
Posters
Room H (Lobby) - 18:10
1. Induction of Belief Rules and Belief Decision Trees from Uncertain Data
by Khalil AbuDahab (*), Yu-Wang Chen, Ling Xu.
2. Systemic Decision Making: A New Holistic Approach in Ahp-Multiactor Decision
Making
by Alfredo Altuzarra (*), Pilar Gargallo, José Mar´ıa Moreno-Jiménez, Manuel Salvador.
3. Impact of Food Price Volatility over Irrigation Agriculture using a Revealed Preference Model
by Carlos Gutierrez-Martin (*), Julio Berbel, Carlos Mario Gómez.
4. Use of Fuzzy ANP-VIKOR Technique in Multi-Criteria Decision-Making of Satisfaction Evaluation for Business-to-Customer (B2C) Services Improvement
by Yuching Chern (*), Gwo-Hshiung Tzeng.
5. A Comparative Study of Four Different Multiple Criteria Analysis Methods through
the Evaluation of Optimal Locations for C&D Waste Management Facilities
by Elena Dosal Vi˜
nas (*), Ana Andrés Payán, Javier Viguri Fuente.
6. Multi-Objective Optimization Problems in Statistical Machine Translation
by Kevin Duh (*), Baskaran Sankaran, Anoop Sarkar.
7. Advanced Metaheuristics for Multi-objective Optimization: Design, Analysis and
Application
by Juan J. Durillo (*).
8. Routing System Optimization – Case Study
by Ever Angel Fuentes Rojas (*), Jeyson Andres Mart´ınez Gamboa.
9. Decision Making using Immune Algorithm for Increasing of Level Crossing Capacity
by Mikhail Gorobetz (*).
10. A Multi Criteria Decision Analysis-Based Methodology for the Risk Assessment of
Flood Hazards at the Regional Scale
by Panagiotis Isigonis (*), Andrea Critto, Valentina Gallina, Antonio Marcomini,
Elena Semenzin, Silvia Torresan, Alex Zabeo.
11. New Directions in Robustness Analysis and Preference Modeling in Multiple Criteria
Decision Aiding
by Milosz Kadzinski (*).
12. Precise Consistency Consensus Matrix in an AHP-Group Decision Making Local
Context
by José Mar´ıa Moreno-Jiménez (*), Juan Aguarón, Mar´ıa Teresa Escobar.
13. Cautious Solutions in Multi-Scenario Bargaining. An Application to Union-Firm
Negotiation.
by Victoriana Rubiales (*), Amparo Mar´ıa Mármol, Luisa Monroy.
12
14. A Bayesian Negotiation Model for Life Testing
by Mar´ıa Jes´
us Rufo (*), Jacinto Mart´ın, Carlos Javier Pérez.
15. DR-MOEA/D: A new Double Reference Point Approach of MOEA/D Based in
Aspiration and Reservation Levels
by Rubén Saborido-Infantes (*), Enrique Alba, Mariano Luque.
16. Hybrid Evolutionary Multi-Objective Optimization for Enhanced Convergence and
Diversity
by Karthik Sindhya (*).
17. Distribution of Federal Funds to Mexican Subnational Governments: Identifying
Tools to Enhance MCDA Process in the Public Security Sector
by E. Ivonne Vergara-Maldonado (*), Gabriel Sánchez-Guerrero.
13
Tuesday - 18th June
Session TU1A (plenary)
Plenary 2
Chaired by Jyrki Wallenius
1. Goal Programming: An Overview of Current Developments and an Application to
Offshore Wind Farm Modelling [09:00 - 10:00]
by Dylan Jones (*).
Session TU2B (contributed)
Group Decision Making
Chaired by Carlos Romero
1. Relational Multi-Attribute Models in DEX Methodology [10:20 - 10:45]
by Trdin Nejc (*), Marko Bohanec.
2. What Matters to Stakeholders and Citizens in the Evaluation of Local Policies?
Using MCDA to Understand Different Perspectives in Town Renewal [10:45 - 11:10]
by Ricardo da Silva Vieira (*), Paula Antunes.
3. Deriving Priority Weights from Pairwise Comparison Matrices under Different Rationality Scenarios [11:10 - 11:35]
by Carlos Romero (*), Jacinto González-Pachón.
Session TU2C (contributed)
Behavioural Issues (I)
Chaired by Gilberto Montibeller
1. The Relative Importance of the Criteria and the Ratio of the Directional Derivatives
[10:20 - 10:45]
by Rafael Sarkisyan (*), Olga Eskova.
2. An Evaluation of the Situation Judgment Test as a Multidimensional Measure of
Decision Making [10:45 - 11:10]
by Lauren Reinerman-Jones (*), Avonie Parchment, Grace Teo.
3. Strategic Multi-Criteria Decision Analysis [11:10 - 11:35]
by Gilberto Montibeller (*).
14
Session TU2D (invited)
Decision/Optimization Models based on Soft Computing (I)
Chaired by Carlos Cruz Corona
1. Fuzzy Meta-Goals in Goal Programming [10:20 - 10:45]
by Mariano Jiménez López (*), Mar Arenas-Parra, Amelia Bilbao.
2. Multi-Objective Restricted Dynamic Vehicle Routing Problem with Time Windows
[10:45 - 11:10]
by Jésica De Armas (*), Belén Melián, José A. Moreno Pérez.
3. Fuzzy Linguistic Multi-Criteria Morphological Analysis applied to Scenario Planning [11:10 - 11:35]
by Carlos Cruz Corona (*), Mar´ıa T. Lamata, Antonio D. Masegosa, Pablo Villacorta.
Session TU2E (invited)
Robust Ordinal Regression (I)
Chaired by Salvatore Greco
1. Information Levels in Additive Group Decision Models under Incomplete Information: Bridging the Cardinal-Ordinal Gap [10:20 - 10:45]
by Luis Dias (*), Paula Sarabando, Rudolf Vetschera.
2. Non Additive Robust Ordinal Regression for Urban and Territorial Planning: an
Application for Siting an Urban Waste Landfill [10:45 - 11:10]
by Marta Bottero (*), Silvia Angilella, Salvatore Corrente, Valentina Ferretti, Salvatore Greco, Isabella Lami.
3. Entropy Measures to Control Robustness in Ordinal Regression Models [11:10 11:35]
by Salvatore Greco (*), Yannis Siskos, Roman Slowinski.
Session TU2F (contributed)
EMO: Economic Aplications
Chaired by Theodor Stewart
1. Dealing with Migratory Flows under Uncertainty: a Multi-objective Optimization
Approach [10:20 - 10:45]
by Pilar Campoy-Mu˜
noz (*), Carlos R. Garc´ıa-Alonso, Melania Salazar-Ordo˜
nez.
2. Robust Selection of Environmental Quality Metrics based on a Multi-Criteria Pareto
Data Mining Process [10:45 - 11:10]
by Angel Udias (*), Javier Cano, Mauricio Chiazzaro, Lorenzo Galbiati, Andrés
Redchuk.
3. Multiobjective Land-Use Planning Based on Proximity Measures [11:10 - 11:35]
by Theodor Stewart (*).
15
Session TU2G (invited)
Multi-Criteria Optimization for the Sustainable Design and Operation of
Dynamic Processes (I)
Chaired by Filip Logist
1. Multi-Objective Optimization of Integrated Design and Control of a Paper Mill
[10:20 - 10:45]
by Ingrida Steponavice (*), Kaisa Miettinen.
2. The Treatment of Actuator Failures in Mechatronic Systems by Multi-objective Optimization Methods [10:45 - 11:10]
by Christian Horenkamp (*), Michael Dellnitz, Sina Ober-Blöbaum, Robert Timmermann.
3. Multi-Objective Optimisation for the Modeling and Control of Uncertain Dynamic
(Bio)Chemical Processes [11:10 - 11:35]
by Filip Logist (*), Thomas Coenen, Ioanna Stamati, Dries Telen, Mattia Vallerio,
Jan Van Impe.
Session TU3B (invited)
AHP/ANP for Sustainability
Chaired by Birsen Karpak
1. A Strategic Information System for Sustainable Supply Chain [11:55 - 12:20]
by Steve Taraszewski (*), Birsen Karpak.
2. Sustainability Assessment and Spatial ANP: a Methodological Proposal for Studying
the New Megacity Region Turin-Milan [12:20 - 12:45]
by Valentina Ferretti (*), Marta Bottero, Giulio Mondini.
3. Multi-Criteria Decision Making Approach for Resource Efficient Bread Supply Chains
[12:45 - 13:10]
by Aleksander Banasik (*), G.D.H. (Frits) Claassen, Argyris Kanellopoulos, Jack
van der Vorst.
4. Prioritizing Third Party Logistics Providers: An Analytical Network Process Approach [13:10 - 13:35]
by Birsen Karpak (*), Ozden Bayazit.
16
MCDM Applications (I)
Chaired by Juan Gaytán-Iniestra
1. On Application of Multi-Criteria Decision Making with Ordinal Information in Elementary Education [11:55 - 12:20]
by Jir´ı Mazurek (*).
2. The Decision Making Model of Service Improvements of Smart Home System based
on IOA-NRM Approach [12:20 - 12:45]
by Chung-An Huang (*).
3. Measuring Performance of European Countries in the Cultural Domain: an MCDM
Approach [12:45 - 13:10]
by Seyhan Nisel (*), Rauf Nisel.
4. A Multicriteria Optimization Model for a Humanitarian Logistics Problem: An Integral Approach [13:10 - 13:35]
by Juan Gaytán-Iniestra (*), Rafael Caballero Fernández, Christopher Mej´ıa-Argueta,
Julián Molina Luque, Bego˜
na Vitoriano.
Session TU3D (contributed)
Environmental Multi-Criteria Decision Making (II)
Chaired by Alfredo G. Hernández-D´ıaz
1. Mixed Generation Sensitivity Analysis: An Optimal Scenario Study [11:55 - 12:20]
by Reginald Wilson (*).
3. Multi-Criteria Decision Support in Paper Production Industry [12:45 - 13:10]
by G.D.H. (Frits) Claassen (*), Johanna Gerdessen.
4. A Multi-objective Waste Collection Problem [13:10 - 13:35]
by Alfredo G. Hernández-D´ıaz (*), Abraham Duarte, Francisco Gortázar, Miguel
´
Angel
Hinojosa, Ana Dolores López-Sánchez.
17
Session TU3E (invited)
New Methods for Multi-Objective Optimization (II)
Chaired by Mariano Luque
1. New Developments of Interactive Multiobjective Optimization using DominanceBased Rough Set Approach [11:55 - 12:20]
by Benedetto Matarazzo (*), Salvatore Greco, Roman Slowinski.
2. Non-Linear Multi-Objective Optimization via Parallel Algorithms: Solving a Planar
Facility Location and Design Problem [12:20 - 12:45]
by Juana López-Redondo (*), José Fernández-Hernández, Aránzazu Gila-Arrondo,
Pilar Mart´ınez-Ortigosa.
3. Reference Point Methods and Approximation of Pareto Sets [12:45 - 13:10]
by Kai-Simon Goetzmann (*), Christina B¨
using, Jannik Matuschke, Sebastian Stiller.
4. E-NAUTILUS: A Surrogate based Interactive Multi-objective Optimisation Method
without Trading Off [13:10 - 13:35]
by Karthik Sindhya (*), Mariano Luque, Kaisa Miettinen, Ana Belén Ruiz, Francisco
Ruiz.
Session TU3F (invited)
Evolutionary Multiobjective Optimization (EMO-II)
1. Using Choquet Integral as Preference Model in Interactive Evolutionary Multiobjective Optimization [11:55 - 12:20]
by Juergen Branke (*), Salvatore Corrente, Salvatore Greco, Roman Slowinski.
2. A New Evolutionary Algorithm for the Bi-objective Ring Star Problem [12:20 12:45]
by Carmen Galé (*), Herminia I. Calvete, José A. Iranzo.
3. GP-DEMO: Differential Evolution for Multiobjective Optimization Based on Gaussian Process Models [12:45 - 13:10]
by Miha Mlakar (*), Bogdan Filipic, Dejan Petelin, Tea Tuˇsar.
4. An Improved MOPSO Algorithm for Multiobjective Bilevel Linear Problems [13:10
- 13:35]
by Maria João Alves (*).
18
Session TU3G (contributed)
Interactive Multi-Objective Optimization (II)
Chaired by Carlos Henggeler Antunes
1. A Comparison of Multi-Objective Approaches to Solve a Parallel Machine Scheduling Problem [11:55 - 12:20]
by Yeliz Buruk (*), Gulcan Gocuklu.
2. Decision Support for Multi-Attribute Multi-Item Reverse Auctions [12:20 - 12:45]
by G¨
ulsah Karakaya (*), Murat Koksalan.
3. Incorporating Stakeholders’ Preferences into Wastewater Infrastructure Planning in
Switzerland [12:45 - 13:10]
by Jun Zheng (*), Judit Lienert.
4. An Interactive Tool for Multi-Objective Linear Programming [13:10 - 13:35]
by Carlos Henggeler Antunes (*), Maria João Alves.
AHP/ANP & DEMATEL
Chaired by Gwo-Hshiung Tzeng
1. The Optimal Resort Development Investment Projects with a Replacement Cost
Perspective [15:05 - 15:30]
by Chiao-Chen Chang (*), T. Tyrone Lin.
2. A Hybrid Dynamic MCDM Model-Tourism Competitiveness Improvement Strategy
[15:30 - 15:55]
by Gwo-Hshiung Tzeng (*), Ming-Tsang Lu, Hsin-Chuan Peng, Kua-Hsin Peng.
3. Combined Dominance-based Rough Set Approach and Fuzzy DEMATEL for Analyzing the Adoption Intention of Tourist Guide Mobile Applications [15:55 - 16:20]
by Yang-Chieh Chin (*).
4. Enhancing Smart Mobile Devices by the Fuzzy DEMATEL based Network Process
[16:20 - 16:45]
by Gwo-Hshiung Tzeng (*), Chi-Yo Huang, Yu-Sheng Kao.
19
Environmental Multi-Criteria Decision Making (III)
Chaired by Valentina Ferretti
1. Comparison of Two Methods for Developing a Multicriteria Evaluation System to
Assess Animal Welfare [15:05 - 15:30]
by Paula Mart´ın Fernández (*), Carlos Buxadé, Joachim Krieter.
2. A Multi-Criteria Aiding Tool for the Assessment of French Statutory Urban Planning based on ELECTRE TRI. A Case Study of the Local Plan of Toulouse, France
[15:30 - 15:55]
by Aurélie Prevost (*), Jean Bandet, Nathalie Molines.
3. Incorporation and Communication of Uncertainty in Decision Support for Environmental Management: A Case Study on Water Quality Improvement [15:55 - 16:20]
by Nele Schuwirth (*), Peter Reichert, Christian Stamm.
4. Best Practices for Spatial Multicriteria Evaluation: a Critical Review [16:20 - 16:45]
by Valentina Ferretti (*), Gilberto Montibeller.
Session TU4D (contributed)
Multi-Objective Optimization Methods (I)
Chaired by Jerzy Michnik
1. A Stochastic Choquet Integral Preference Model: SMAA-Choquet [15:05 - 15:30]
by Salvatore Corrente (*), Silvia Angilella, Salvatore Greco.
2. Multiobjective Variational Problems Involving Generalized Higher Order Functions
[15:30 - 15:55]
by Kalpana Shukla (*).
3. Multiobjective Optimization with Combined Global and Local Metamodeling [15:55
- 16:20]
by Shapour Azarm (*), Weiwei Hu, Khaled Saleh.
4. Multi-project scheduling as a MCDM problem [16:20 - 16:45]
by Bogumila Krzeszowska (*), Tadeusz Trzaskalik.
20
Session TU4E (contributed)
Multi-Attribute Utility Theory (II)
Chaired by Rakesh Sarin
1. Multiattribute Utility Analysis using Strict Preference Relations Elicited from a
Decision Maker [15:05 - 15:30]
by Nishizaki Ichiro (*), Ohmi Masakazu, Hayashida Tomohiro.
2. Using MACBETH with the Choquet Integral to Model Interdependencies between
Indicators in the Context of Risk Management [15:30 - 15:55]
by Diana F. Lopes (*), Carlos Bana e Costa, Mónica Duarte Oliveira.
3. Determinants of Experienced Utility: Laws and Implications [15:55 - 16:20]
by Rakesh Sarin (*), Manel Baucells.
EMO: Expensive Evaluation Functions
Chaired by Julia Handl
1. Optimizing Multi-objective Mixed Integer Programs using Evolutionary Algorithms
[15:05 - 15:30]
by Thomas Stidsen (*).
2. Automating Mechanism Design using Evolutionary Multi-Criteria Optimisation [15:30
- 15:55]
by Arjun Chandra (*), Richard Allmendinger, Peter Lewis.
3. A Neural Network based Hybrid Evolutionary Multiobjective Optimization Algorithm for Computationally Demanding Problems [15:55 - 16:20]
by Jussi Hakanen (*), Tommi Kokko, Karthik Sindhya.
4. Redundant Phenotype-Objective Space Mappings in Multi-Criterion Data-Clustering
[16:20 - 16:45]
by Julia Handl (*), Agah John, Joshua Knowles.
21
Socially Responsible Investment Decision Making (II)
Chaired by Blanca Pérez-Gladish
1. Practical Measurement of Environmental and Social Achievement Levels for Mutual
Funds: A Synthetic Indicator with its Software Application [15:05 - 15:30]
by David Pla Santamaria (*), Milagros Bravo Sellés, Ana M. Garc´ıa Bernabeu,
Blanca Pérez-Gladish.
2. Assessing the Efficiency of Equity Mutual Funds. A Fuzzy DEA Approach [15:30 15:55]
by Ismael Baeza-Sampere (*), Vicente Coll-Serrano, Bouchra M’Zali, Paz Méndez
Rodr´ıguez.
3. Portfolio Multiple Criteria Analysis, Ranking and Optimal Selection [15:55 - 16:20]
by Cristinca Fulga (*).
4. A Compromise Programming Model for Mutual Funds’ Socially Responsible Portfolio Selection [16:20 - 16:45]
by Blanca Pérez-Gladish (*), Rosario Balaguer, Ana M. Garc´ıa Bernabeu, Paz
Méndez Rodr´ıguez.
AHP/ANP in Practice (I)
Chaired by Michael Bruhn Barfod
1. A Framework for Sustainable for Third Party Logistic Supplier Identification [17:05
- 17:30]
by Birsen Karpak (*).
2. Assessment of Renewable Energies based on Fuzzy and Qualitative Multi-Criteria
Decision Making: a Comparison of two Wethodologies [17:30 - 17:55]
by Arayeh Afsordegan (*), N´
uria Agell, Lázaro V. Cremades, Monica Sanchez, Siamak Zahedi.
4. Combining MCDA and Risk Analysis: Dealing with Scaling Issues in the Multiplicative AHP [18:20 - 18:45]
by Michael Bruhn Barfod (*), Kim Bang Salling, Rob Van Den Honert.
22
Environmental Multi-Criteria Decision Making (IV)
Chaired by Valerie Belton
1. How to Support Environmental Management by MCDM Techniques? [17:05 - 17:30]
by Peter Reichert (*), Simone Langhans, Judit Lienert, Nele Schuwirth.
2. Optimizing Economic and Environmental Performances of Solar Power and Electric
Vehicles: A MOMILP Application [17:30 - 17:55]
by Ellen De Schepper (*), Xavier Gandibleux, Sebastien Lizin, Benjamin Martin,
Steven Van Passel, Thomas Vincent.
3. A MAUT Approach for the Management and Remediation of a Site Contaminated
by Uranium Processing Activities [17:55 - 18:20]
by Miguel C. Mart´ın (*), Antonio Jiménez, Alfonso Mateos, Danyl Pérez-Sánchez.
4. Climate Change - a Challenge for MCDA? [18:20 - 18:45]
by Valerie Belton (*).
Session TU5D (invited)
Decision Support for Supply Chain Risk Management
Chaired by Tina Comes
1. A Hybrid Method for Supplier Order Quantity Allocation [17:05 - 17:30]
by R. J. Kuo (*), C. M. Bai, T. L. Hu.
2. Price-setting Under Risk Criteria for Virtual Products and Services [17:30 - 17:55]
by Mordecai Henig (*), Tal Avinadav, Tatyana Chernonog.
3. Minmax Robustness for Multi-Objective Optimization Problems [17:55 - 18:20]
by Jonas Ide (*), Matthias Ehrgott, Anita Schöbel.
4. Scenario-based Multi-Criteria Decision Support for Robust Humanitarian Relief
Supply Chains [18:20 - 18:45]
by Tina Comes (*), Frank Schätter.
23
Session TU5E (contributed)
MCDM Applications (II)
Chaired by Ahmet Kandakoglu
1. Multiobjective Optimization of High-Performance Reinforced Concrete I-Beams by
Simulated Annealing [17:05 - 17:30]
by V´ıctor Yepes (*), Julián Alcalá, Tatiana Garc´ıa-Segura.
2. Multi-Criteria IMRT Optimisation based on the Generalised EUD Model [17:30 17:55]
by Guillermo Cabrera G. (*), Matthias Ehrgott, Andrew Mason.
3. Comparing the Scalarization Methods for Bi-Objective Assembly Line Scheduling
Problem [17:55 - 18:20]
by Emine Akyol (*), Tugba Sara¸c.
4. A Performance Monitoring System based on a Combined MCDM Methodology
[18:20 - 18:45]
by Ahmet Kandakoglu (*), Makbule Kandakoglu.
Multi-Objective Optimization Methods (II)
Chaired by Serpil Sayin
1. Improved Constraint Handling Technique for Multi-Objective Optimization [17:05
- 17:30]
by Vaibhav Maheshwari (*), Gade Pandu Rangaiah, Shivom Sharma.
2. Computing Nadir Point for Multiobjective Discrete Optimization Problems [17:30 17:55]
by Gokhan Kirlik (*), Serpil Sayin.
3. Solving Linear Fractional Programming Problems Using Multiobjective Optimization Techniques [17:55 - 18:20]
by Maryam Hasannasab (*), Matthias Ehrgott.
4. Identifying Supported and Extreme Supported Nondominated Solutions and an Empirical Analysis of their Representative Quality [18:20 - 18:45]
by Serpil Sayin (*).
24
Multi-Criteria Shortest Path Problems: Theory and Applications (I)
Chaired by Lorenzo Mandow
1. Engineering Multiobjective Shortest Path Heuristics [17:05 - 17:30]
by Christos Zaroliagis (*).
2. Multicriteria Evacuation Plan for Natural Disasters [17:30 - 17:55]
by Ismaila Abderhamane Ndiaye (*), Emmanuel Neron.
3. Engineering Parallel Bi-Criteria Shortest Path Search [17:55 - 18:20]
by Stephan Erb (*).
4. Searching Graphs with Lexicographic Goal References [18:20 - 18:45]
by Lorenzo Mandow (*), José L. Pérez-de-la-Cruz, Francisco Javier Pulido.
25
Wednesday - 19th June
Session WE1B (contributed)
AHP/ANP in Practice (II)
Chaired by Valerio Salomon
1. An Integrated AHP&VIKOR Method for Hospital Site Selection [09:00 - 09:25]
by Hafize Yilmaz (*), Sait G¨
ul, Ilker Topcu.
2. Assessment of the Relationship between Planned Obsolescence and Product EcoDesign based on MCDA [09:25 - 09:50]
by Pedro Nicolás Casas Páez (*), Felix Antonio Cortes Aldana.
R The AHP Project on the Cloud [09:50 - 10:15]
3. Elephas:
´
by Fernando Pérez-Rodr´ıguez (*), Angeles
Camino-Saco, Esteban Gómez-Garc´ıa,
Alejandro Mosquera, Alberto Rojo-Alboreca.
4. Standardization of Classroom Furniture in a Higher Education Institution [10:15 10:40]
by Valerio Salomon (*), Pedro Alonso, Fernando Marins.
Session WE1C (contributed)
Data Envelopment Analysis (I)
Chaired by Pekka Korhonen
1. Quality of the Information Provided by Fuzzy Data in the Efficiency Evaluation of
the Spanish Textile Industry [09:00 - 09:25]
by Ismael Baeza-Sampere (*), Vicente Coll-Serrano.
2. Production Points with the Same Reference Hyperplane in DEA: Applications in
Sensitivity Analysis and Returns to Scale Classification [09:25 - 09:50]
by Akram Dehnokhalaji (*), Nasim Nasrabadi, Majid Soleimani-Damaneh.
3. An Algorithm to Determine Efficiency Scores in Large DEA Data Sets [09:50 - 10:15]
by Gazi Bilal Yildiz (*), Banu Soylu.
4. On a Linear Transformation of Variables in Data Envelopment Analysis [10:15 10:40]
by Pekka Korhonen (*), Abolfazl Keshvari.
26
Session WE1D (invited)
Multi-Criteria Decision Making in the Energy Sector
Chaired by Valentin Bertsch
1. Analytical Approximation of the Influence of Capacity on the Economic and Ecological Performance of Bioenergy Plants [09:00 - 09:25]
by Lars-Peter Lauven (*).
2. Multi-Criteria Decision Support for Designing Critical Infrastructure Stress Tests
[09:25 - 09:50]
by Tina Comes (*), Valentin Bertsch.
3. Simulation-Based Innovization for Improving Productivity and Energy Efficiency of
Manufacturing Systems [09:50 - 10:15]
by Amos Ng (*), Kalyanmoy Deb, Catarina Dudas, Ainhoa Goienetxea, Matias
Urenda Moris.
4. On the Need for Multi-Criteria Modelling in Power Systems Analysis and Planning
[10:15 - 10:40]
by Valentin Bertsch (*), Wolf Fichtner.
Session WE1E (invited)
Promoting Sustainability with MCDM
Chaired by Luiz F. Autran M. Gomes
1. Green Economy in the State of Rio de Janeiro - A Multicriteria Evaluation [09:00 09:25]
by Rogerio Valle (*), João Cl´ımaco.
2. Sustainable Supply Chains: Contributions from MCDM [09:25 - 09:50]
by Birsen Karpak (*).
3. MCDA-GIS Integration for the Evaluation of the Invasive Alien Species. The Case
of the Eastern Grey Squirrel in Umbria (Central Italy) [09:50 - 10:15]
by Lucia Rocchi (*), Gianluca Massei, Daniele Paoloni.
4. Promoting Sustainability in Mining: Multicriteria Assignment of Resources in Vale
[10:15 - 10:40]
by Luiz F. Autran M. Gomes (*), Marcelo G. C. Macedo, Roberto Camanho,
Thomaz M. Camanho, Luiz Geraldo Biagioni Martins.
27
Session WE1F (invited)
Decision/Optimization Models based on Soft Computing (II)
Chaired by Carlos Cruz Corona
1. A Decision Support System for Fuzzy Portfolio Selection [09:00 - 09:25]
by Carlos Ivorra (*), Clara Calvo, Vicente Liern.
2. Explicit Equations for Non-Dominated Frontier in a Portfolio Selection Problem
based on Soft Computing [09:25 - 09:50]
by Alberto Carlos Salda˜
na (*), José Manuel Cadenas, Maria del Carmen Garrido,
Raquel Martinez.
3. Fuzzy Multi-Criteria Decision Models for the Portfolio Selection Problem [09:50 10:15]
by Enriqueta Vercher (*), José D. Berm´
udez.
4. Fuzzy Decentralized Production and Distribution Planning in an Uncertain Environment [10:15 - 10:40]
by Manuel Diaz-Madro˜
nero (*), Josefa Mula, David Peidro.
Session WE1G (contributed)
Multi-Objective Linear Programming
Chaired by Petr Fiala
1. About an Implementation of a 3-Objective Linear Programming Solver [09:00 09:25]
by Sebastian Schenker (*).
2. Robust Weighted Solutions for Multi-Objective Linear Programming [09:25 - 09:50]
by Wlodzimierz Ogryczak (*).
3. A Semidefinite Programming Approach for Solving Multiobjective Linear Programming [09:50 - 10:15]
by Victor Blanco (*), Safae El-Haj Ben-Ali, Justo Puerto.
4. Dynamic Multi-Objective De Novo Linear Programming [10:15 - 10:40]
by Petr Fiala (*).
28
Session WE2B (contributed)
Outranking Methods
Chaired by Roman Slowinski
1. Rank Reversal in the PROMETHEE Methods: a Summary of Recent Investigations
[11:00 - 11:25]
by Yves De Smet (*).
2. The SMAA-PROMETHEE Methods [11:25 - 11:50]
by Salvatore Corrente (*), Jose Figueira, Salvatore Greco.
3. Measuring Attractiveness of High School Programs using Composite Index of Attractiveness and a Multi-level Outranking Framework: Evidence from PISA Survey
in Serbia [11:50 - 12:15]
by Mladen Stamenkovic (*), Ivan Anic, Marko Backovic, Zoran Popovic.
4. Solving Ranking Problems with ELECTRE-III in Case of Hierarchical Family of
Criteria [12:15 - 12:40]
by Luis Del Vasto (*), Roman Slowinski, Aida Valls, Piotr Zielniewicz.
Session WE2C (contributed)
Applications of Fuzzy Sets
Chaired by Ling Xu
1. A Fuzzy Numbers Regression for Inducing OECD Wellbeing to WCY Competitiveness 2012 [11:00 - 11:25]
by Hsin-Hui Wen (*), Yu-Chien Ko, Gwo-Hshiung Tzeng.
2. A Multi-Criteria Decision Analysis Methodology based on Multi-Attribute Value
Theory and Fuzzy Logic for Quantitatively Scoring the Reliability of Ecotoxicological Data [11:25 - 11:50]
by Panagiotis Isigonis (*), Philippe Ciffroy, Andrea Critto, Antonio Marcomini,
Elena Semenzin, Alex Zabeo.
3. The Evaluation of Competitiveness of Visegrad Four NUTS 2 Regions by the Hybrid
Eigenvalue-Fuzzy Cognitive Map Approach [11:50 - 12:15]
by Zuzana Kiszova (*), Jir´ı Mazurek, Jan Nevima.
4. An Extension of the Fuzzy DEMATEL Method for Group Decision Making [12:15
- 12:40]
by Tarifa Almulhim (*), Ludmil Mikhailov, Ling Xu.
29
Session WE2D (invited)
Applications & Developments of Multicriteria Decision Models
Chaired by Marcelo Alencar
1. A MCDA Model to Support Public Safety Management Policy [11:00 - 11:25]
by Caroline Mota (*), André Gurgel.
2. Plaster Waste Destination Problem in the Buildings Sites: An Application of the
VFT Methodology [11:25 - 11:50]
by Luciana Hazin (*), Marcelo Alencar, Caroline Mota.
3. A Multi-Objective Genetic Algorithm for Integrated Scheduling of Production and
Maintenance Activities for a Single Machine [11:50 - 12:15]
by Rodrigo Ferreira (*), Adiel Almeida-Filho, Cristiano Cavalcante.
4. Natural Gas Pipelines: A Decision Support System to Evaluate Multidimensional
Risk [12:15 - 12:40]
by Marcelo Alencar (*), Adiel Almeida, Thárcylla Clemente, Eduardo Krym.
Session WE2E (contributed)
MCDM Applications (III)
Chaired by Enrique Alba
1. Mixed Integer Multi-Objective Optimization for Flight and Maintenance Planning
of Mission Aircraft [11:00 - 11:25]
by Andreas Gavranis (*), George Kozanidis.
2. Multicriteria Modeling and Optimization of a Market Place of Leads [11:25 - 11:50]
by Maamar Manel (*), Vincent Mousseau, Wassila Ouerdane.
3. Optimizing Telecommunications in Vehicular Networks with a Parallel Multiobjective PSO [11:50 - 12:15]
by Jamal Toutouh (*), Enrique Alba.
30
Session WE2F (invited)
Multi-criteria Shortest Path Problems: Theory and Applications (II)
Chaired by Lucie Galand
1. Multivalued Heuristics for One-To-One Biobjective Shortest Path Problems [11:00
- 11:25]
by Enrique Machuca (*), Lorenzo Mandow.
2. Multi-Criteria Timetable Information - Extending the Pareto Approach [11:25 11:50]
by Mathias Schnee (*), Mohammad Hossein Keyhani.
3. Solving Robust Bicriteria Shortest Path Problems [11:50 - 12:15]
by Andrea Raith (*), Kenneth Kuhn, Marie Schmidt, Anita Schöbel.
4. Bidirectional versus Unidirectional Heuristic Search for Multiojective Optimization
in State Space Graphs [12:15 - 12:40]
by Lucie Galand (*), Anisse Ismaili, Patrice Perny, Olivier Spanjaard.
Session WE2G (invited)
Strategic Decisions with Multiple Criteria
Chaired by Amparo Mar´ıa Mármol
1. An Evaluation of Website Upgrade Options. A Case Study Comparison of ANFIS
and RIMER [11:00 - 11:25]
by Ling Xu (*), Yu-Wang Chen, Andrada Sabin.
2. Location Decision for Firm Expansion: A Bicriterion Approach [11:25 - 11:50]
by Blas Pelegr´ın (*), Pascual Fernández Hernández, Mar´ıa Dolores Garc´ıa Pérez.
3. Estimating the Pareto Front of a Hard Bi-Criterion Competitive Location Problem
[11:50 - 12:15]
by Algirdas Lancinskas (*), Pascual Fernández Hernández, Blas Pelegr´ın, Julius
Zilinskas.
4. Pareto Equilibria in Cournot Competition Under Uncertainty [12:15 - 12:40]
by M. Angeles Caraballo (*), Eva Buitrago, Amparo Mar´ıa Mármol, Luisa Monroy.
31
Thursday - 20th June
Session TH1A (plenary)
Plenary 3
Chaired by Pekka Korhonen
1. Public Decisionmaking and Decision Conferencing [09:00 - 10:00]
by Carlos Bana e Costa (*).
Session TH2B (contributed)
Bi-Criteria Optimization: Methodological Aspects
Chaired by Kim Allan Andersen
1. A Two Objective Classification Approach based on Conic Functions [11:00 - 11:25]
by Gurkan Ozturk (*), Refail Kasimbeyli.
2. Improved Upper Bounds for a Two-Phase Biobjective Shortest Path Algorithm
[11:25 - 11:50]
by F. Antonio Medrano (*), Richard Church.
3. A Bi-Objective Mixed-Binary Set Covering Problem [11:50 - 12:15]
by Banu Soylu (*).
4. The Bi-Criterion Adaptive Stochastic Knapsack Problem [12:15 - 12:40]
by Kim Allan Andersen (*), Matthias Ehrgott, Lars Relund Nielsen, Daniele Pretolani.
Session TH2C (contributed)
Data Envelopment Analysis (II)
Chaired by Josef Jablonsky
1. Evaluating Clinical Performance of Pediatric Emergency Physicians with a Help of
Data Envelopment Analysis Model [11:00 - 11:25]
by Wojtek Michalowski (*), Ken Farion, Fiallos Javier, Jonathan Patrick.
2. Ranking Candidates through Variable Convex Sequences of Weights [11:25 - 11:50]
by Bonifacio Llamazares (*).
3. Value Efficiency: A General Case [11:50 - 12:15]
by Majid Soleimani-Damaneh (*).
4. Re-Measuring the Happy Planet Index using DEA and MCDM Models [12:15 12:40]
by Josef Jablonsky (*).
32
Session TH2D (invited)
Fuzzy Goal Programming
Chaired by Mariano Jiménez López
1. An Optimization Model for Group Decision Making: Weighting and Ranking with
Incomplete Fuzzy Preference Relations [11:00 - 11:25]
by Mauricio Ruiz-Tagle (*), Esther Dopazo.
2. Comparing Fuzzy Goal Programming Approaches for Collaborative Supply Chain
Master Planning [11:25 - 11:50]
by Manuel Diaz-Madro˜
nero (*), Josefa Mula, David Peidro.
3. Solving Fuzzy Mathematical Programming Problems by a Parametric Approach: a
Historical View [11:50 - 12:15]
by Carlos Cruz Corona (*), Ricardo Coelho Silva, José Luis Verdegay Galdeano.
4. A Group Decision Making Model Based in Goal Programming with Fuzzy Hierarchies. An Application to Regional Forest Planning [12:15 - 12:40]
by Mariano Jiménez López (*), Mar Arenas-Parra, Amelia Bilbao, Mar´ıa Victoria
Rodr´ıguez-Ur´ıa.
Session TH2E (invited)
Multi-Objective Optimization of Computationally Expensive Problems
Chaired by Jussi Hakanen
1. Distributed Computation of Pareto Points in Multiobjective Programming [11:00 11:25]
by Margaret Wiecek (*), Brian Dandurand.
2. Handling Computational Variability in the Evaluation of Objectives: Ideas and
Prospects [11:25 - 11:50]
by Julia Handl (*), Richard Allmendinger, Joshua Knowles, Anna Lavygina.
3. Properties and Benefit of the Linear Efficient Frontier Approximation in the Objective Space [11:50 - 12:15]
by Volker Maag (*), Tabea Grebe, Uwe Nowak.
4. Agent Assisted Interactive Multiobjective Optimization for Computationally Demanding Problems [12:15 - 12:40]
by Vesa Ojalehto (*), Kaisa Miettinen, Dmitry Podkopaev.
33
Session TH2F (contributed)
Non Linear Multi-Objective Optimization
Chaired by Kathrin Klamroth
1. On Continuation Methods for Non-Linear Multi-Objective Optimization [11:00 11:25]
by Benjamin Martin (*), Alexandre Goldsztejn, Laurent Granvilliers, Christophe
Jermann.
2. Optimization over the Weakly Efficient Set of a Multiobjective Programming Problem Constrained by Quadratic Functions [11:25 - 11:50]
by Syuuji Yamada (*), Tamaki Tanaka, Tetsuzo Tanino.
3. Habitat Availability and Spatial Quality as Conflicting Objectives of Multi-use Forest Management [11:50 - 12:15]
by Dmitry Podkopaev (*), Artti Juutinen, Adriano Mazziotta, Kaisa Miettinen,
Mikko Mönkkönen, Pasi Reunanen, Olli-Pekka Tikkanen.
4. A Multiple Objective View on Robust Optimization and Stochastic Programming
with Finite Scenario Sets [12:15 - 12:40]
by Kathrin Klamroth (*), Elisabeth Köbis, Anita Schöbel, Christiane Tammer.
Session TH2G (invited)
Representation of the Efficient Set with Quality Guarantees
Chaired by Luis Paquete
1. An Efficient Box Algorithm for Discrete Tricriteria Optimization Problems [11:00 11:25]
by Kerstin Dächert (*), Kathrin Klamroth.
2. Finite Representation of Non-Dominated Sets in Multi-Objective Linear Programming [11:25 - 11:50]
by Matthias Ehrgott (*), Lizhen Shao.
3. Transforming Constraints into Objectives: Experiments with Bidimensional Knapsack Problems [11:50 - 12:15]
by Britta Schulze (*), Kathrin Klamroth, Luis Paquete.
4. Finding Representative Subsets in Multiobjective Discrete Optimization [12:15 12:40]
by Luis Paquete (*), An´ıbal Ponte, Michael Stiglmayr, Daniel Vaz.
34
Session TH3B (invited)
Robust Ordinal Regression (II)
Chaired by Roman Slowinski
1. Multiple Criteria Hierarchy Process for ELECTRE TRI Methods [14:10 - 14:35]
by Salvatore Corrente (*), Salvatore Greco, Roman Slowinski.
2. Stochastic Ordinal Regression for Multiple Criteria Sorting Problems [14:35 - 15:00]
by Milosz Kadzinski (*), Tommi Tervonen.
3. Inference of Parsimonious Preference Models in Robust Ordinal Regression [15:00 15:25]
by Roman Slowinski (*), Salvatore Greco, Vincent Mousseau.
Classification
Chaired by N´
uria Agell
1. Feature Selection for Classification Using Multi-Objective Optimization [14:10 14:35]
by Emre Cimen (*), Gurkan Ozturk.
2. Multi-Criteria Preference Disaggregation Analysis for Classification: an Application
to Model Colour Preferences [14:35 - 15:00]
by Mohammad Ghaderi (*), N´
uria Agell, Monica Sanchez, Francisco Javier Ruiz.
3. Increasing the Transparency of Model Selection in Multi-Criterion Data-Clustering
[15:00 - 15:25]
by Julia Handl (*), K.Nadia Papamichail.
Session TH3D (contributed)
Behavioural Issues (II)
Chaired by Alexis Tsoukiàs
1. A New Dominance Measuring Method for MCDM with Ordinal Information About
DM’s Preferences [14:10 - 14:35]
by Ernesto Aguayo (*), Antonio Jiménez, Alfonso Mateos.
2. The Support of Decision Criteria Weights Elicitation based on their Probabilistic
Characteristics [14:35 - 15:00]
by Jakub Brzostowski (*), Ewa Roszkowska, Tomasz Wachowicz.
3. Primitive and Derived Information in Decision Models [15:00 - 15:25]
by Alexis Tsoukiàs (*), Alberto Colorni.
35
Session TH3E (invited)
Multi-Criteria Optimization for the Sustainable Design and Operation of
Dynamic Processes (II)
Chaired by Filip Logist
1. Bi-Criteria Optimisation using Surrogate Modelling for Dynamic Process Design
[14:10 - 14:35]
by Eric Fraga (*), Joakim Beck.
2. Multi-Objective Framework for Model Based Design of Experiments: Application
to Hemodialysis and Type 1 Diabetes Model [14:35 - 15:00]
by Vaibhav Maheshwari (*), Gade Pandu Rangaiah, Lakshminarayanan Samavedham.
3. Applying of Fuzzy Logic to Decision-Making Control of the Ship Motion [15:00 15:25]
by Jozef Malecki (*).
EMO for Engineering Design
Chaired by Ansgar Dietermann
1. A Tabu Search Algorithm for Multi Objective Open Vehicle Routing Problem [14:10
- 14:35]
by Erdener Ozcetin (*), Gurkan Ozturk.
2. Efficient Optimisation of a Vehicle Suspension by Hybrid Nature Inspiring Method
[14:35 - 15:00]
by Darakhshan Jabeen Syeda (*).
3. Structured Development of Automotive Electric/Electronic Architectures Using Evolutionary Optimization [15:00 - 15:25]
by Ansgar Dietermann (*), Bernard Bäker.
Session TH3G (invited)
Decision Making for the Use of Natural Resources (I)
Chaired by José Antón
1. A Multicriteria Decision Aiding Methodology to Support Public Administration on
Sustainable Energy Action Plans [14:10 - 14:35]
by Maria Franca Norese (*), Giuliano Dall’O, Chiara Novello.
2. Comparison between Continuous and Discrete Multicriteria Decision Methods applied to Lands Use, Conservation, Management and Planning on Hydro-Basin in
Cordoba Province (Argentina) [14:35 - 15:00]
by Juan Grau (*), Diego Andina, José Antón, José Manuel Cisneros, Ana Tarquis.
3. Some Remarks about Multi-Criteria Methods into Planning for Use and Services of
Natural Areas [15:00 - 15:25]
by José Antón (*), Diego Andina, José Manuel Cisneros, Juan Grau, Roland Portait,
Maria Sánchez, Ana Tarquis.
36
Session TH4B (contributed)
AHP/ANP Applications to Development Economics
Chaired by José A. Gómez-Limón
1. Decomposing Value Creation when Assessing Investments: a Multi-Criteria Approach [15:45 - 16:10]
by Mar´ıa Dolores Guerrero-Baena (*), José A. Gómez-Limón.
2. A System Approach for Green Supplier Selection Based on the ANP Model with
BOCR for the Electronics Industry in Taiwan [16:10 - 16:35]
by Hsu-Shih Shih (*), Chiau-Ching Chen, Yi-Chun Lin.
3. A Three-Stage Methodology to Analyze the Global Competitiveness of a Sector
[16:35 - 17:00]
by Ozay Ozaydin (*), Bora Cekyay, Ozgur Kabak, Sule Onsel Ekici, Fusun Ulengin.
4. Analyzing the Provision of Agricultural Public Goods: The Case of Irrigated Olive
Groves in Southern Spain [17:00 - 17:25]
by Anastasio Villanueva-Rodr´ıguez (*), Manuel Arriaza, José A. Gómez-Limón,
Olexandr Nekhay.
Sustainability Analysis
Chaired by Jian-Bo Yang
1. New Alternative Aggregation Formulas for the Human Development Index: a Multicriteria Approach [15:45 - 16:10]
by Beatriz Rodr´ıguez D´ıaz (*), Mar´ıa José Angulo-Guerrero, Mariano Luque, Salvador Pérez-Moreno.
2. Design Safer and Greener Road Projects by using a Multi-Objective Optimization
Approach [16:10 - 16:35]
by Renaud Sarrazin (*), Yves De Smet.
3. Sustainable National Transport Planning – Governance with MCDA? [16:35 - 17:00]
by Anders Vestergaard Jensen (*), Steen Leleur, Joe Zietsman.
4. The Derivation of Weights for Sustainability Criteria: A Framework for Corporate
Assessment in Sugar Manufacturing [17:00 - 17:25]
by Panitas Sureeyatanapas (*), David Bamford, Jian-Bo Yang.
37
Session TH4D (contributed)
Integer Multi-Objective Optimization
Chaired by Johanna Gerdessen
1. Web-Based Decision Support System for Solving Multiple Objective Linear Integer
Problems (MOLIPs) [15:45 - 16:10]
by Leoneed Kirilov (*), Krasimira Genova, Vassil Gouliashki, Boris Staykov.
2. On Branch and Bound Approach to Multi-Objective Global Optimization [16:10 16:35]
by Antanas Zilinskas (*), Julius Zilinskas.
3. Multicriteria 0-1 Knapsack Problems with K-min Objectives [16:35 - 17:00]
by Aiying Rong (*), Jose Figueira, Kathrin Klamroth.
4. MCDM in Nutritional Epidemiology [17:00 - 17:25]
by Johanna Gerdessen (*), Aleksander Banasik, G.D.H. (Frits) Claassen.
Session TH4E (contributed)
MCDM Applications (IV)
Chaired by Jose Roberto Ribas
1. Using Methods of Multi-Criteria Decision Making to Evaluate the Sustainability of
Bioenergy Concepts by Integrating Adequate Reference Points [15:45 - 16:10]
by Nils Lerche (*), Jutta Geldermann.
2. Web Multicriteria Spatial Decision Support System: Integration of a Web-based
ELECTRE TRI in ArcGIS [16:10 - 16:35]
by Sandra Silva (*), Lu´ıs Al¸cada-Almeida, Luis Dias.
3. Unsupervised Update of the Preferences on Numerical and Multi-Valued Categorical
Criteria from the Analysis of the User Behaviour [16:35 - 17:00]
by Aida Valls (*), David Isern, Lucas Marin, Antonio Moreno.
4. An Assessment of Multipurpose Water Resources Using Multicriteria Analysis [17:00
- 17:25]
by Jose Roberto Ribas (*), Mariana Pinheiro, Juliana Ribas Severo.
38
EMO in Industrial Engineering
Chaired by Fouad Ben Abdelaziz
1. A Genetic Algorithm for a Multi-Objective Parallel Machine Scheduling Problem
with Special Process Constraints [15:45 - 16:10]
by Tugba Sara¸c (*), Yakup Emre Aydinbeyli, M¨
uzeyyen Melek Ko¸canli.
2. A Genetic Algorithm Approach for Solving Multi-Objective Factory Layout Problem
[16:10 - 16:35]
by Phong Ho (*), Truong Huy, Ho Quoc.
3. Multiple Criteria Decision Making in Anti-Collision Scheduling for Level Crossing
Tasks [16:35 - 17:00]
by Anatoly Levchenkov (*).
4. Multi-Objective Task Allocation using Multi-Agent Coalition Formation [17:00 17:25]
by Fouad Ben Abdelaziz (*), Noha Amer.
Session TH4G (contributed)
Bi-Criteria Optimization: Applications
Chaired by Jacques Teghem
1. A Column Generation based Algorithm for a Bi-Objective Capacity Evaluation of
a Railway Infrastructure [15:45 - 16:10]
by Evgeny Gurevsky (*), Xavier Gandibleux, Aurélien Merel.
2. A Bi-Objective Decomposition Method for Solving the Bi-Objective Multi-Commodity
Minimum Cost Flow Problem [16:10 - 16:35]
by Siamak Moradi (*), Matthias Ehrgott, Andrea Raith.
3. Bi-objective Traffic Assignment with Multiple User Classes: A Time Surplus Approach [16:35 - 17:00]
by Olga Perederieieva (*), Matthias Ehrgott, Andrea Raith, Judith Y. T. Wang.
4. A biobjective Parallel Machine Scheduling Problem with Eligibility, Ready Dates
and Delivery Times [17:00 - 17:25]
by Jacques Teghem (*), Manuel Mateo.
39
Friday - 21st June
Session FR1A (plenary)
MCDM Society Awardees Session
Chaired by Murat Köksalan
Session FR2B (contributed)
AHP/ANP: Methodological Issues (II)
Chaired by Gang Kou
1. A Bayesian Analysis Method for AHP based on Group Decision Making [11:50 12:15]
by Gang Kou (*), Changsheng Lin, Yi Peng.
2. Structural Modeling Approach to Multiple Criteria Problems with Interrelated Components [12:15 - 12:40]
by Jerzy Michnik (*).
3. Comparative Analysis and Evaluation of Multi-Criteria Decision Making Methods
[12:40 - 13:05]
by Gang Kou (*), Daji Ergu, Thomas L. Saaty.
Session FR2C (invited)
Robust Ordinal Regression (III)
Chaired by Vincent Mousseau
1. Learning the Parameters of a Multiple Criteria Sorting Method from Large Sets of
Assignment Examples [11:50 - 12:15]
by Olivier Sobrie (*), Vincent Mousseau, Marc Pirlot.
2. Robust Ordinal Regression in Geographical Information Systems [12:15 - 12:40]
by Salvatore Greco (*), Antonio Boggia, Salvatore Corrente, Gianluca Massei, Roman Slowinski.
3. Robust Elicitation of a Qualitative Ranking Model using Inconsistent Data [12:40 13:05]
by Wassila Ouerdane (*), Jinyan Liu, Vincent Mousseau.
40
Session FR2D (contributed)
Applications of Multi-Attribute Utility Theory
Chaired by Mario Castillo
1. Modeling Consumer Preferences about Vehicles with Multi-Attribute Additive Models: Survey-Based Experiments [11:50 - 12:15]
by Luis Dias (*), Gabriela Oliveira, Paula Sarabando.
2. Factors Affecting Marketing Channel Choice Decisions in Citrus Supply Chain: A
Conceptual Framework [12:15 - 12:40]
by Muhammad Imran Siddique (*), Elena Garnevska, Norman Marr.
3. Use of Multiattribute Utility Function for a Cost-Utility Analysis Kangaroo Mother
versus “Traditional” Care for Premature Infants in Bogota, Colombia [12:40 - 13:05]
by Mario Castillo (*), Astrid Bernal, Nathalie Charpak, Mar´ıa-Adelaida Córdoba,
John-Jairo R´ıos, Juan-Gabriel Ruiz, Tammy Trujillo.
Session FR2E (invited)
Methods with Guaranteed Performance for Multi-Objective Combinatorial Optimization (II)
Chaired by Daniel Vanderpooten
1. Two-Phase Method for Multiobjective Mixed 0-1 Linear Programming [11:50 - 12:15]
by Thomas Vincent (*), Xavier Gandibleux, Anthony Przybylski.
2. Generation of the Choquet Optimal Set of Multiobjective Combinatorial Optimization Problems [12:15 - 12:40]
by Thibaut Lust (*), Antoine Rolland.
3. On Approximate Kernels of Minimal Size for Bicriteria Problems [12:40 - 13:05]
by Florian Jamain (*), Cristina Bazgan, Daniel Vanderpooten.
Session FR2F (invited)
Evolutionary Multiobjective Optimization (EMO-III)
1. Satisfying Multiple Objectives in Wastewater Treatment Network Design Using a
Diversity Preserving Evolutionary Algorithm [11:50 - 12:15]
by Hana Chmielewski (*), S. Ranji Ranjithan.
2. An Evolutionary Multiobjective Optimization Algorithm for Diverse Solutions [12:15
- 12:40]
by Hana Chmielewski (*), Brian E.B. Piper, S. Ranji Ranjithan.
3. Cross Entropy for Combinatorial Optimization Problems with Linear Relaxations
[12:40 - 13:05]
by Julián Molina Luque (*), Rafael Caballero Fernández, Alfredo G. HernándezD´ıaz, Manuel Laguna.
41
Session FR2G (contributed)
Preference Modeling
Chaired by Andrzej M.J. Skulimowski
1. Experimental Evaluation of Polynomial and Copula Functions for Qualitative Option Ranking [11:50 - 12:15]
by Marko Bohanec (*), Biljana Mileva-Boshkoska.
2. A Preference-Based Facility Location Problem [12:15 - 12:40]
by Katarzyna Krupinska (*).
3. Proximity-Based Decision Rules for Multicriteria Model Predictive Control Problems [12:40 - 13:05]
by Andrzej M.J. Skulimowski (*).
Session FR3B (contributed)
Goal Programming/Compromise Programming
Chaired by Luis A. Manotas
1. Multicriteria Model for Selecting Renewable Energy Projects [14:35 - 15:00]
by Diego F. Manotas (*), Carlos A. Lozano, Carlos Vidal.
2. A Sequential Goal Programming Model for University Timetable Construction [15:00
- 15:25]
by Veronika Skocdopolova (*).
3. Multicriteria Model for Locating Distribution Centers [15:25 - 15:50]
by Luis A. Manotas (*), Diego F. Manotas , Germán Ocampo, Carlos Vidal.
4. A Multi-Objective Route Choice Model for Health-Conscious Cyclists [15:50 - 16:15]
by Judith Y. T. Wang (*), Alan K. L. Cheung, Kim N. Dirks, Matthias Ehrgott,
Jon Pearce.
Session FR3C (contributed)
Integer and Combinatorial Multi-Objective Optimization
Chaired by Banu Lokman
1. A New Exact Method for Optimizing a Linear Function over an Integer Efficient
Set [14:35 - 15:00]
by Djamal Chaabane (*).
2. A Fuzzy Model for Project Portfolio Selection [15:00 - 15:25]
by Fátima Pérez (*), Rafael Caballero Fernández, Trinidad Gómez, Vicente Liern.
3. An Interactive Approach to the Bi-objective Inventory Routing Problem [15:25 15:50]
by Sandra Huber (*), Martin Josef Geiger, Marc Sevaux.
4. Finding Nadir Points in Multi-objective Integer Programs [15:50 - 16:15]
by Banu Lokman (*), Murat Koksalan.
42
Session FR3D (invited)
Multi-Objective Optimization and Decision Making under Uncertainty
Chaired by Juergen Branke
1. Risk-Averse Bi-Objective Optimization via Stochastic Dominance under Transaction Costs [14:35 - 15:00]
by Walter Gutjahr (*).
2. Multi-Objective Optimization of Combinatorial Problems with Fuzzy Data [15:00 15:25]
by Oumayma Bahri (*), Nahla Ben Amor, Talbi El-Ghazali.
3. Stochastic Approch Versus Optimizition Over Efficient Set [15:25 - 15:50]
by Fatma Mebrek (*), Djamal Chaabane.
Session FR3E (contributed)
Risk MCDM Modeling (II)
Chaired by Shuang Liu
1. A Multicriteria Prediction Model for Project Classifications [14:35 - 15:00]
by Rueben Laryea (*).
2. Applying the Utadis Method to the Identification of Key Factors of Organisational
Commitment: A Case Study in Petrobras [15:00 - 15:25]
by Luiz F. Autran M. Gomes (*), Marcos E.L. Gon¸calves, Luis A.D. Rangel.
3. Multi-Criteria Sorting with Category Size Restrictions [15:25 - 15:50]
¨
by Selin Ozpeynirci
(*), Murat Koksalan, Vincent Mousseau.
4. Using Structured Decision Making to Facilitate Environmental Risk Management
[15:50 - 16:15]
by Shuang Liu (*).
43
Session FR3F (contributed)
Water Resources Management
Chaired by Eun-Sung Chung
1. Risk Assessment of Large Scale Hydroelectric Plant Projects using Fuzzy Analytical
Hierarchy Process [14:35 - 15:00]
by Jose Roberto Ribas (*), Juliana Ribas Severo, Flavio Sohler.
2. Multi-Criteria Decision Analysis for Water Supply Infrastructure Planning under
Uncertainty [15:00 - 15:25]
by Lisa Scholten (*), Judit Lienert.
3. Rewarding Decisions Beyond the Performance Targets Paradigm [15:25 - 15:50]
by Victor Sousa (*), Ana Faustino.
4. Fuzzy VIKOR Approach for Assessing the Vulnerability of the Water Supply to
Climate Change and Variability in South Korea [15:50 - 16:15]
by Eun-Sung Chung (*), Boram Lee, Kwang-Jae Won.
Session FR3G (invited)
Decision Making for the Use of Natural Resources (II)
Chaired by Juan Grau
1. Classification of Disasters Effects and Humanitarian Decision-Makers’ Criteria [14:35
- 15:00]
by Tinguaro Rodriguez (*), Javier Montero, Bego˜
na Vitoriano.
2. Discussion of the Results Obtained in Several Real Applications using PROMETHE
I and II versus Weighted PROMETHE and other Outranking Methods [15:00 - 15:25]
by Juan Grau (*), Diego Andina, José Antón, Federico Colombo, Lisandro De los
R´ıos, Ana Tarquis.
3. Development of Mathematical Multicriteria Methods to Aid to Elaborate Plans of
Rural Sustainable Development in Areas With Aborigines Communities [15:25 15:50]
by José Antón (*), Diego Andina, Juan Grau, José Luis Rubio, Ana Tarquis.
4. Development of an Innovative Environmental Education Program for Urban Solid
Waste Management through Multi-Criteria Decision Methods (MCDM) [15:50 16:15]
by Juan Grau (*), Diego Andina, José Antón, Rodrigo Martin, Alfredo Soletti, Ana
Tarquis.
Closing Session
44
Abstracts
45
FR2B (contributed)
A Bayesian Analysis Method for AHP based on Group
Decision Making
Kou, Gang (*)
Lin, Changsheng
Peng, Yi
UESTC
The Analytic Hierarchy Process (AHP) is widely used in Group Decision Making (GDM).
The aggregation of individual judgments (AIJ) and the aggregation of individual priorities (AIP) are two of the most widely employed aggregation methods in AHP based
on GDM. The overall priority vector can be obtained by many different methods such
as weighted geometric mean method (WGMM), goal programming approach (GPA) and
decision support model (DSM) etc. Different from these methods, in this paper, we propose a new Bayesian analysis method (BAM) for the AIP, which makes full use of the
prior distribution for the parameters and sample information while complying with the
Pareto principal. We also develop a multiplicative model with log-normal errors under
the hypothesis of the existence of consensus among the decision makers and evaluate the
statistical properties of the priority vector obtained by employing the posterior distribution. Besides, a measurement to assess the group consistency is proposed. The proposed
BAM provides a flexible framework and efficient estimates for obtaining the priority vector. Three numerical examples are used to illustrate the applications and advantages of
the BAM. The results are compared with those produced by the other techniques according to Euclidean distance (ED), Minimum Violation (MV) and Mean absolute deviation
(MAD). The results show that the proposed BAM is more flexible, practical and realistic
than the other methods.
Keywords: Aggregation Of Individual Priorities, Analytic Hierarchy Process, Group
Decision Making.
46
MO5H (poster)
A Bayesian Negotiation Model for Life Testing
Rufo, Mar´ıa Jes´
us (*) Mart´ın, Jacinto
Pérez, Carlos Javier
University of Extremadura
This work presents a Bayesian negotiation model in the context of life testing. A general
situation in which a manufacturer offers a product to a consumer is considered. The reliability of the product depends on its lifetime. Furthermore, the consumer can use her/his
own lifetime distribution to address the uncertainty about the information provided by
the manufacturer with respect to the quality of the product. Hence, the consumer might
accept o reject the product in advance. By other hand, the manufacturer would have to
convince the consumer of the product reliability. Then, the manufacturer offers the consumer a sample of size n to make life testing. Based on this sample, the consumer must
decide to accept or reject the product. In order to solve the problem, a Bayesian sequential
model of negotiation is proposed here. Firstly, it is considered that the parametric lifetime
distributions belong to a subclass of the exponential family. Thus, a unified framework
is carried out. For these distributions, different prior distributions for the manufacturer
and the consumer are considered. The utility functions for both, the manufacturer and
the consumer, are based on an observable life length. Moreover, the utility function for
the manufacturer includes the corresponding costs. A simulation-based approach is implemented to calculate the optimal sample size n that the manufacturer will offer the
consumer. Finally, an application is presented and discussed in order to show that the
proposed technique is easily applied in practice.
Keywords: Bayesian Analysis, Exponential Family, Life Testing, Negotiation.
47
TH4G (contributed)
A Bi-Objective Decomposition Method for Solving the
Bi-Objective Multi-Commodity Minimum Cost Flow
Problem
Moradi, Siamak (*) Raith, Andrea
The University of Auckland
Ehrgott, Matthias
Lancaster University
We address linear minimum cost flow problems with two objectives and multiple commodities. We present a new method for solving the problem by integrating the Dantzig-Wolfe
decomposition method in the iterations of the bi-objective simplex method. The standard
bi-objective simplex method is initialized by optimizing the problem with respect to the
first objective. The method then iteratively finds entering variables with the maximum
ratio of improvement of the second objective over the deterioration of the first objective.
The method stops when all the efficient extreme solutions are obtained. Our proposed
method has a similar structure where the initial solution can be obtained by solving a
single objective multi-commodity flow problem with the existing Dantzig-Wolfe decomposition method. The method then continues iteratively by solving subproblems with
only single commodity network constraints and a ratio objective function. The optimal
solution of each subproblem which is a variable may be a candidate for entering the basis.
Among them we choose the one with the best ratio objective value to ensure the method
moves from one extreme point to the next. We also demonstrate that the optimal solution of each subproblem can be found among the efficient solutions of a linear bi-objective
problem.
Keywords: Bi-Objective Multi-Commodity Flow Problem, Bi-Objective Simplex Method,
Decomposition Method.
48
TH2B (contributed)
A Bi-Objective Mixed-Binary Set Covering Problem
Soylu, Banu (*)
Erciyes University
In this study, a bi-objective mixed-binary set covering problem is considered and algorithms are developed to find Pareto solutions of this problem. There are two decisions in
our case: one is to decide on which sets should be selected and the other is to determine
some parameter levels for the selected sets. Although the problem is originally mixed
binary nonlinear programming problem, it can be linearized. The Pareto frontier of this
problem has some interesting properties. We propose two methodologies: one is based on
the decomposition approach and the other is based on the branch-and-bound algorithm.
Both methods can also be generalized to find Pareto solutions of mixed integer linear
programming (MILP) problems. We present our numerical experiments and findings.
Keywords: Branch and Bound, Mixed Integer Programming, Set Covering Problem.
49
TH4G (contributed)
A biobjective Parallel Machine Scheduling Problem
with Eligibility, Ready Dates and Delivery Times
Teghem, Jacques (*)
University of Mons
Mateo, Manuel
Universitat Politècnica de Catalunya
The shop has three parallel machines of levels 1, 2 and 3 respectively corresponding to
decreasing quality production. The jobs to process on one of the machines have processing times - identical for all the machines -, ready dates and delivery times. These jobs
correspond also to one of the three levels meaning that a job of level l is eligible to be
assigned only to the machines of levels 1 till l. Nevertheless if a job is assigned to a
machine of level k different than 1, a penalty k - 1 is incurred. Two criteria are considerd:
the makespan and the total penalty. The aim is to determine or to approximate the set
of efficient schedules for the minimization of these two criteria.
Keywords: Multi-Objective Optimization, Production Scheduling.
50
MO3E (contributed)
A Careful Look at the Importance of Criteria and
Weights
Wallenius, Jyrki (*) Korhonen, Pekka
¨ orni, Anssi Silvennoinen, Kari
O¨
Aalto University
We investigate the connection between weights, scales, and the importance of criteria,
when a linear value function is assumed to be a suitable representation of a decision
maker’s preferences. Our considerations are based on a simple two-criteria experiment,
where the participants were asked to indicate which of the criteria was more important,
and to pairwise compare a number of alternatives. We use the participants’ pairwise
choices to estimate the weights for the criteria in such a way that the linear value function explains the choices to the extent possible. More specifically, we study two research
questions: 1) is it possible to find a general scaling principle that makes the rank order of
the importance of criteria consistent with the rank order of the magnitudes of the weights,
and 2) how good is a simple, direct method of asking the decision maker to “provide”
weights for the criteria compared to our estimation procedure. Our results imply that
there is reason to question two common beliefs, namely that the values of the weights
would reflect the importance of criteria, and that people could reliably “provide” such
weights without estimation.
Keywords: Behavioral, Criterion Importance, Multi-Attribute Value Theory, Weights.
51
TH4G (contributed)
A Column Generation based Algorithm for a
Bi-Objective Capacity Evaluation of a Railway
Infrastructure
Gurevsky, Evgeny (*)
Gandibleux, Xavier
Merel, Aurélien
University of Nantes
A capacity evaluation of a railway infrastructure is one of the important issues of the
preliminary planning for the corresponding railway traffic. Given a train schedule and an
additional circulation demand, it consists in estimating the maximal number of trains that
can pass through the infrastructure while satisfying the possible exploitation constraints
and conflicts. In the literature, this task has received the name of saturation problem.
In the current context of the European railway market which are going to be open to
competing companies, the saturation problem becomes an essential economic challenge.
Thus, an efficient management of a railway infrastructure has to be done not only maximizing its capacity but in such a manner as to also minimize the disparity between the
companies using this infrastructure. Taking into account this argument, the problem becomes bi-objective. To model this problem, we use the set packing formulation, where
the decision variables ask if a train is able to use a railway within a given time-period
and the constraints describe all the conflicting situations. In the case of a real-world
application, such formulation is of extremely big size due to an exponential number of
train assignments. As a consequence, for its exact resolution, a good quality of bound
sets is needed. In this work, for the problem considered, we study the linear programming (LP) relaxation that can be viewed as its bound set. To solve the LP relaxation,
the column generation (CG) procedure is used. During the presentation, we will mention
some difficulties of applying CG for the bi-objective linear programming problems and
will show different ways of constructing bound sets based on the information provided.
The computational experiments will be carried out on a collection of benchmark data.
The results will be compared and discussed.
Keywords: Applications of MCDM, Bicriteria Model, Large Scale Optimization.
52
MO5H (poster)
A Comparative Study of Four Different Multiple
Criteria Analysis Methods through the Evaluation of
Optimal Locations for C&D Waste Management
Facilities
Dosal Vi˜
nas, Elena (*)
Andrés Payán, Ana
Viguri Fuente, Javier
University of Cantabria
Specific legislation has been developed in the last years for the improvement of Construction and Demolition Waste (C&DW) management, and therefore, recycling facilities
must be located. Selection of a non-appropriate location is a critical aspect which affects
long-term profits and can lead into environmental and social impacts as well. For this
reason, this decision must be studied thoroughly, taking into account several targets and
aspects that usually are opposite. Multiple Criteria analysis (MCA) is usually applied
to compare scenarios regarding to contradictory objectives, and a wide range of different methods are defined, with different forms to calculate the total value score for an
alternative. In this work, a comparative study of four different MCA methods; Weighted
Summation, Regime, Electre II and Evamix; was performed by means its application to
select optimal locations for C&DW management facilities in Cantabria, Spain. Results
obtained are analyzed in order to assess if the method applied may provide different results with the same data, which may lead to a non-objective identification of the best
alternative. Besides, analyses of the sensitivity and uncertainty of the results have been
also carried out in order to investigate the robustness of the results obtained. Uncertainty
of input data includes uncertainty in data selection and uncertainty in measurement values, whereas uncertainty in the model itself includes model structure and parameters.
The main results show that the application of more than one MCA method to verify the
results is highly recommended; mainly to locate C&DW recycling facilities due to this
decision involves a large investment that must be insured for a long time. Acknowledgments. This work has been supported by R+D project “Establecimiento del Sistema de
Indicadores para el Flujo Sostenible de Recursos y Residuos en la Comunidad Autónoma
de Cantabria”, Government of Cantabria-University of Cantabria, Spain.
Keywords: Comparative Analysis, Construction And Demolition Waste, Multi-Criteria
Decision Making, Recycling Facilities, Sensitivity Analysis, Uncertainty.
53
TU3G (contributed)
A Comparison of Multi-Objective Approaches to Solve
a Parallel Machine Scheduling Problem
Buruk, Yeliz (*)
Eskisehir Osmangazi University
Gocuklu, Gulcan
Although there are many problems that are well-known for single objective optimization,
the real world problems have more than one conflicting objectives. For such problems,
various multi-objective techniques take part in the literature and there is not a known
proven optimal solution method for all kinds of problems. In the scope of this study,
a multi-objective parallel machine scheduling problem has been considered. WeightedTchebycheff, epsilon-constraint and conic scalarization methods were used to solve the
problem by using GAMS v.12.1 software package. A set of solutions was identified to
represent the trade-off between different considerations. The computational results for
each technique have been compared in terms of efficiency and applicability.
Keywords: Mathematical programming, Multi-Objective Optimization, Scalarization,
Scheduling.
54
TU4G (invited)
A Compromise Programming Model for Mutual
Funds’ Socially Responsible Portfolio Selection
Pérez-Gladish, Blanca (*)
University of Oviedo
Garc´ıa Bernabeu, Ana M.
Universidad Politécnica de Valencia
Balaguer, Rosario
Universitat Jaume I
Méndez Rodr´ıguez, Paz
Socially Responsible Investing (SRI) corresponds to an investment practice that takes into
account not only the usual return-risk criteria, but also other non-financial dimensions,
namely in terms of environmental, social and governance concerns. However, while a diverse set of models has been developed to support investment decision-making based on
financial criteria like risk and return, models including also socially responsible criteria are
rather scarce. In this paper we propose a multicriteria portfolio selection model for mutual funds based on Compromise Programming which takes into account both, financial
and SRI criteria. The proposed model is intended to be an individual investment decision
making tool for mutual funds’ portfolio selection taking into account the subjective and
individual preferences about different financial and non-financial features of an individual
investor. Few portfolio selection models include among their decision making criteria non
financial ones. In order to do so, the first problem to be solved is the measurement of the
degree of social responsibility of a mutual fund. The second one is how to incorporate the
former one as a decision criterion in the portfolio selection model and how to elicit from
the particular investor his/her personal preferences regarding the different financial and
non financial criteria. In this work, we have tried to address these problems. In order
to illustrate the suitability and applicability of the proposed investment decision making
model, an empirical study on a set of US domiciled equity mutual funds is carried out.
Keywords: Compromise Programming, Equity Mutual Funds, Multi-Criteria Decision
Making, Portfolio Selection, Socially Responsible Investment.
55
MO4B (invited)
A Conic Scalarization Method in Multi-Objective
Optimization
Kasimbeyli, Refail (*)
Anadolu University
This paper presents the conic scalarization method for scalarization of nonlinear multiobjective optimization problems. We introduce a special class of monotonically increasing
sublinear scalarizing functions and show that the zero sublevel set of every function from
this class is a convex closed and pointed cone which contains the negative ordering cone.
We introduce the notion of a separable cone and show that two closed cones (one of them
is separable) having only the vertex in common can be separated by a zero sublevel set
of some function from this class. It is shown that the scalar optimization problem constructed by using these functions enables to characterize the complete set of efficient and
properly efficient solutions of multi-objective optimization problems without convexity
and boundedness conditions. The conic scalarization method guarantees a most preferred
efficient or properly efficient solution, if a suitable scalarizing parameter set consisting of
a weighting vector, an augmentation parameter, and a reference point is chosen.
Keywords: Achievement Scalarizing Functions, Multi-Objective Optimization, NonLinear Multi-Objective Optimization, Scalarization, Weighted Sum.
56
MO3B (invited)
A Decision Model for Identifying and Prioritizing the
Capability Gaps in Defense Planning
Kandakoglu, Ahmet (*)
Turkish Air Force Command
Kahraman, Y.Rıza
Topcu, Ilker
Istanbul Technical University
Capability-Based Planning (CBP) is a systematic approach to force development that
aims to propose the most appropriate force options to meet the defense priorities. The
starting point of this approach is to identify and prioritize the requirements and gaps in
operational military capabilities. The more accurately these requirements and gaps are
quantified, the better force options are developed and resources are distributed. Therefore,
the decision-makers require analytical tools based on quantitative methodologies for managing this critical and highly complex process in an effective and timely manner. In this
study, the aforementioned problem is extended to the multiple criteria decision-making
(MCDM) environment in order to be systematically analyzed and quantified. A decision
model based on Analytic Hierarchy Process (AHP) method is developed for identifying
and prioritizing the capability gaps. In the model, first, the capability requirements with
respect to possible scenarios are identified. The AHP method is applied to weight these
capabilities from the subjective judgments of the experts. Then, the capability gaps are
assessed between the identified requirements and the current or programmed force capabilities. Finally, the gaps are prioritized using the weights of the capabilities to provide
valuable insights for the planning actions to fulfill these gaps in the CBP process.
Keywords: Analytic Hierarchy Process, Capability Based Planning, Multi-Criteria Decision Analysis.
57
WE1F (invited)
A Decision Support System for Fuzzy Portfolio
Selection
Ivorra, Carlos (*) Calvo, Clara Liern, Vicente
Universidad de Valencia
We present a Decision Support System for fuzzy portfolio selection with cardinality and
semi-continuous variable constraints based on a multi-criteria approach. It is intended
for investors interested in socially responsible investment that assume as main criterion
an adequate trade-off between expected return and risk but they are willing to consider
alternative similar solutions taking into account several diversification or non-profit goals.
To this end, we present a fuzzy efficient frontier to the investor where he or she can see
the best options in a selected zone as well as the consequences of slight movements with
regard to the various criteria involved.
Keywords: Fuzzy Mathematical Programming, Multi-Criteria Decision Making, Portfolio Selection.
58
TU5B (contributed)
A Framework for Sustainable for Third Party Logistic
Supplier Identification
Karpak, Birsen (*)
Youngstown State University
Recent research (among 40 large 3PL companies) illustrated that 3PL suppliers are committed themselves to environmental sustainability goals. 3PL customers have also shown
increasing interest in the environmental sustainability capabilities of the 3PL suppliers.
The choice of location such as offshore vs. onshore, close to the customer vs. close to the
raw material source as well as the choice of mode of transportation can have a significant impact on the GHG emissions. Carbon footprint receives the most attention. Social
aspects and their integration into business seems to be much less advanced and therefore even more difficult to trace than environmental aspects. The economic dimension
was regarded as most important, stemming from the argument that without economic
success, there would be no supply chains. A Multiple criteria model has been proposed
for 3PL suppliers considering economic, environmental and social dimensions of sustainability. Criteria as well as potential 3PL suppliers will be prioritized with an illustrative
example.
Keywords: 3PL, Analytic Network Process, Multi-Criteria Decision Making, Sustainability, Sustainable Supply Chains.
59
FR3C (contributed)
A Fuzzy Model for Project Portfolio Selection
Pérez, Fátima (*)
Caballero Fernández, Rafael
University of Málaga
Gómez, Trinidad
Liern, Vicente
Decision makers usually have to face a budget and other types of constraints when they
have to decide which projects are going to be undertaken (to satisfy their requirements
and guarantee profitable growth). Our purpose is to assist them in the task of selecting
projects portfolios. We have approached this problem by proposing a general nonlinear
binary multi-objective mathematical model, which takes into account all the most important factors mentioned in the literature related with Project Portfolio Selection and
Scheduling. Due to the existence of uncertainty in different aspects involved in the aforementioned decision task, we have also incorporated into the model some fuzzy parameters,
which allow to represent information not fully known by the decision maker/s. The resulting problem is both fuzzy and multiobjective. The results are complemented with
graphical tools, which show the usefulness of the proposed model to assist the decision
maker/s.
Keywords: Fuzzy Mathematical Programming, Multi-Objective Optimization, Project
Portfolio Selection.
60
WE2C (contributed)
A Fuzzy Numbers Regression for Inducing OECD
Wellbeing to WCY Competitiveness 2012
Wen, Hsin-Hui (*)
Chung Hua University
Ko, Yu-Chien
Chung Hua University
Tzeng, Gwo-Hshiung
Kainan University
The better life could be the basics of the national competitiveness. In order to verify
this assumption, an induction of crossing systems is proposed to illustrate the conditional dependence of better life on competitiveness. The technique of crossing systems is
built on fuzzy numbers that associate criteria values to decision objects. Based on a homogeneous–scaling of the fuzzy numbers, a classification regression can make inductions
of crossing systems available. For an illustration instance, Organization for Economic
Co-operation and Development (OECD) better life and World Competitiveness Yearbook
(WCY) competitiveness in 2012 are used as dataset, dominance-based rough set approach
(DRSA) is designed to give the fuzzy numbers. The result reveals that the better life does
not have significant evidences to support national competitiveness; contrarily, the national
competitiveness has the regression evidence on the better life effectively. The weights of
the regression further help users to realize the priorities of the aggregated competitiveness.
Keywords: Competitiveness, Fuzzy Numbers, Fuzzy Set Extension Of DRSA, Wellbeing.
61
TH4F (contributed)
A Genetic Algorithm Approach for Solving
Multi-Objective Factory Layout Problem
Ho, Phong (*)
Vietnam National University-HCMC
Huy, Truong Quoc, Ho
Vietnam National University-HCM
Facility layout is a significant decision which is associated with manufacturing system
performance. Traditionally, approaches to layout design attempted to minimize the total transport distance among the facilities. Recently, the facility layout is viewed as a
multi-objective optimization problem to meet the competitive environment. However,
the existing works have not paid an attention to the flexibility of the manufacturing system. The research is aimed at using multi-objective optimization in the context of a
factory layout problem with new proposed criterion namely reachability, an element used
to evaluate the ability of the system to reach to another facility when the current facility is
out of order. The optimization model is built based on Genetic Algorithms (GAs) to solve
the factory layout problem which is modeled as a multi-objective optimization problem
– minimizing total travel time, maximizing total closeness rating scores, and maximizing
total layout flexibility. A numerical example is presented to demonstrate the effectiveness
of GAs approach as well as the application of the reachability criterion in facility layout
problems.
Keywords: Factory Layout, Genetic Algorithms, Multi-Objective Optimization, Reachability.
62
TH4F (contributed)
A Genetic Algorithm for a Multi-Objective Parallel
Machine Scheduling Problem with Special Process
Constraints
Sara¸c, Tugba (*)
Aydinbeyli, Yakup Emre Ko¸canli, M¨
uzeyyen Melek
In this study, a special type of identical parallel machine scheduling problem with n products and m identical machines is considered to determine that the products should be
produced in which machine and order in a biscuit factory. The multi-objective goal programming model with special process constraints is proposed for this problem. Since size
of this kind of problems is usually large in real life, they cannot be solved with exact
solution methods. Therefore, a genetic algorithm with an original crossover operator is
developed for solving this problem. The effectiveness of the developed solution approach
is tested on small, medium and large size randomly generated test instances. Finally,
obtained results for the real-life problem are discussed.
Keywords: Scheduling.
63
TH2D (invited)
A Group Decision Making Model Based in Goal
Programming with Fuzzy Hierarchies. An Application
to Regional Forest Planning
Jiménez López, Mariano (*)
Universidad del Pa´ıs Vasco
Arenas-Parra, Mar
Bilbao, Amelia Rodr´ıguez-Ur´ıa, Mar´ıa Victoria
Universidad de Oviedo
A multi-criteria group decision making model is presented. In this model the relative
importance between the different decision makers in the group is imprecise. To address
this problem a goal programming (GP) approach is used. We suppose that each decision
maker has its preferred solution, obtained applying any multi-objective programming
technique (compromise programming, weighted GP, lexicographic GP, extended lexicographic GP, GP with fuzzy hierarchies, ...). These solutions are used as targets in an
aggregated GP model in which the normalized unwanted deviations are interpreted in
terms of degree of achievement of the targets. Using this approach fuzzy binary relations
are employed to model the imprecise hierarchy between decision makers. This way a GP
model with fuzzy hierarchies (FHGP) is built. The solution of this model is proposed as
a consensus decision. To show the applicability of our proposal a regional forest planning
problem is tackled. The objective is determining tree species composition in order to improve the values achieved by Pan-European sustainable indicators. This problem involves
competing uses and stakeholders which, regarding the aforementioned indicators, have a
different preference scheme. In the first place a decision model for each stakeholder is
solved. Therefore their corresponding preferred plan is obtained. These preferred plans
are aggregated in a GP model in which the fuzzy hierarchy between stakeholders is incorporated. The solution of this FHGP model is offered to the stakeholders as a consensus
plan.
Keywords: Decision Maker’s Preferences, Fuzzy Sets, Goal Programming, Group Decisions Making.
64
TU4B (contributed)
A Hybrid Dynamic MCDM Model-Tourism
Competitiveness Improvement Strategy
Tzeng, Gwo-Hshiung (*) Lu, Ming-Tsang
Kainan University
Peng, Kua-Hsin
Peng, Hsin-Chuan
Yuanpei University
Multiple Criteria Decision Making (MCDM) is scientific analytical method that can help
decision-makers for evaluating and selecting the best alternative based on multiple criteria. However, traditional MCDM have some limitations/defects for solving the real-world
problems. First, conventional MCDM assumes independent criteria with a hierarchical structure. Second, conventional MCDM only obtains relatively good solutions from
existing alternatives. Third, conventional MCDM merely allows the selection and ranking of alternatives or strategies. Therefore, a Hybrid Dynamic MCDM combined with
DANP (DEMATEL-based ANP) method must be developed to overcome the defects of
the traditional MCDM method. Finally, an empirical case is presented to demonstrate
the effectiveness of a Hybrid Dynamic MCDM model combining DANP and VIKOR for
evaluating tourism competitiveness of four Asian countries, namely Japan, Malaysia, Singapore and Taiwan to identify competitiveness gaps and explore strategies for improving
tourism competitiveness. Decision makers should increase the priority of the cause criteria based on the influential relation map in advance, and to successfully create a high
tourism competitiveness to achieve the aspired/desired levels.
Keywords: Analytic Network Process, Competitiveness, DEMATEL, Hybrid Approaches,
Multi-Criteria Decision Making, Multi-Criteria Evaluation.
65
TU5D (invited)
A Hybrid Method for Supplier Order Quantity
Allocation
Kuo, R. J. (*)
Bai, C. M.
Hu, T. L.
Taiwan University of Science and Tech.
With fiercely global competition, most of manufacturing functions in supply chain have
become more and more important. Especially materials purchasing, it is a critical link
between outsourcing and manufacturing. Thus, an efficient purchasing can continuously
help enterprise enhance efficiency, reduce cost and increase profit. However, in consideration of risk diversification, the current manufacturing considers multiple source suppliers.
In selection of key suppliers, most enterprises attempt to obtain the best product quality
and maximize interest of supply chain partners. Thus, selecting suitable suppliers can
provide high-quality product and reduce total costs for the enterprises. Thus, supplier
selection and order quantity allocation are still two important issues for the current manufacturing sector. Therefore, this study intends to propose a two-stage approach for order
allocation. In the first stage, one of the association rule mining techniques, TD-FP-growth
algorithm, is employed to select important suppliers from the existing suppliers and determine the importance of each supplier. Then, a hybrid of artificial immune network
(Opt-aiNet) and particle swarm optimization (PSO) (aiNet-PSO) is proposed to allocate
the order quantity for the key suppliers with minimum cost by considering multiple criteria including purchasing cost, ordering cost, delivery cost, and defective material cost. In
order to verify the proposed two-stage approach, a case company’s daily purchasing ledger
focusing on the consumer electronic product manufacturers is employed. The computational results indicate that TD-FP-growth algorithm is able to select the key suppliers
using the historical data. Besides, the proposed hybrid method, aiNet-PSO, can provide
lowest cost compared to those of genetic algorithm, PSO, and artificial immune system.
Thus, the proposed approach is able to help case company reduce the outsourcing cost.
Keywords: Allocation, Data Mining, Decision Support Systems, Evolutionary Computations, Hybrid Approaches, Supply Chain Management.
66
TU5C (contributed)
A MAUT Approach for the Management and
Remediation of a Site Contaminated by Uranium
Processing Activities
Mart´ın, Miguel C. (*) Jiménez, Antonio Mateos, Alfonso
Technical University of Madrid
Pérez-Sánchez, Danyl
CIEMAT
Pridneprovsky Chemical Plant, located at Dneprodzerzhinsk (Ukraine), was one of the
largest uranium processing enterprises of the former USSR, producing a huge amount of
uranium residues stored in 9 tailing impoundments and contaminating different buildings.
The processing of uranium stopped in 1990, due to the disintegration of the USSR, and
since then financial resources were not put in remediation programs until 2003. Nowadays,
more than 20 enterprises not related to uranium processing activities are in operation at
this territory and a residential area is located rather close to the zone. Since 2003, the
Government of Ukraine began a systematic investigation of this territory. Environment
monitoring and site surveillance programs were established in 2005. During 2008-09, preliminary safety assessment for the identified hazards was been carried out and the resulting
strategies are being implemented during the State Remediation Programme (2010-14),
for which an international consortium of experts was created within the framework of
ENSURE projects. Among the remediation problems that still are waiting for optimal
strategy to be chosen are the management and decommissioning of the high contaminated
buildings, the largest tailing at Dnieprovskoe, which is partly covered with thick layer of
phosphogypsum and also the wet uranium tailing at Suhachevskoe, still partly covered
with water. The situations described above are complex decision-making problems in
which several economical, social and environmental conflicting objectives must be taken
into account simultaneously, being necessary a formal analysis. We have used the GMAA
DSS based on an additive multi-attribute utility model to deal with these problems. It
accounts for uncertainty about the strategy impacts and admits incomplete information
about the decision-makers’ preferences. The system provides different sensitivity analyses
that take advantage of the imprecise inputs to reach a final recommendation.
Keywords: Applications of MCDM, Multi-Attribute Utility Theory, Restoration, Uranium Processing.
67
WE2D (invited)
A MCDA Model to Support Public Safety
Management Policy
Mota, Caroline (*) Gurgel, André
Unoversidade Federal de Pernambuco
Public safety planning is a subject of great interest in the modern society. This paper
contributes with an strategic issue for public safety management, regarding the designation of different public policies according to different areas in one region. Therefore, this
article proposes a multicriteria decision model that seeks to prioritize areas for a given region using demographic and socioeconomic issues. Consequently, it’s possible to perform
different strategies in a region according to a set of factors that can influence criminality.
The model was applied in a Brazilian city, where it was divided into human development
units and resulting in 62 different zones of criminality.
Keywords: Multi-Criteria Decision Making, Outranking Methods, Public Safety.
68
MO3D (contributed)
A Model for a Spatial Forest Problem Taking into
Account Economic and Environmental Objectives
Hernandez, Monica (*)
University of Malaga
Gómez, Trinidad
Leon, M. Amparo
University of Pinar del Rio
Molina Luque, Julián
In this work, a forest harvesting planning problem is solved taking into account economic
factors of timber production, environmental protection and other aspects related to. The
forest problem is formalized via a non linear multiobjective programming model, in which
carbon captured is considered, and taking into account spatial constraints aimed at limiting the maximum adjacent area to which clearcutting can be applied. The final outcome
is a highly complex problem that is solved by applying a metaheuristic method. The
model proposed is applied to a real timber production plantation in Cuba and also to a
series of hypothetical forests, randomly generated.
Keywords: Forest Management, Fractional Programming, Harvest Scheduling, MultiObjective Optimization.
69
MO5H (poster)
A Multi Criteria Decision Analysis-Based
Methodology for the Risk Assessment of Flood
Hazards at the Regional Scale
Isigonis, Panagiotis (*) Critto, Andrea Gallina, Valentina
Marcomini, Antonio Semenzin, Elena Torresan, Silvia Zabeo, Alex
Ca’Foscari University of Venice
In recent years, the frequency of water-related disasters has increased and flood events in
Europe (e.g. 2002 in central Europe, 2007 in UK, 2010 in Italy) have caused physical, environmental and socio-economic damages. The current European legislation (e.g. Floods
Directive) enforces the establishment of frameworks for the assessment and management
of flood risks and aims towards the reduction of the consequences for human health, the
environment, the cultural heritage and the economic activity associated with floods in the
European Union. In this context, within the KULTURisk project (FP7), a Regional Risk
Assessment (RRA) methodology is proposed to evaluate the benefits of risk prevention
in terms of reduced environmental risks due to floods, through a GIS-based tool that
produces maps of the total risk index for the regions under analysis. The methodology
is based on the KULTURisk framework and allows the identification and prioritization
of targets (i.e. people, buildings, infrastructures, agriculture, natural and semi-natural
systems, cultural heritages) and areas at risk from floods in the considered region by
comparing the baseline scenario (i.e. current state) with alternative scenarios (i.e. where
different structural and/or non-structural measures are planned). The RRA methodology
employs Multi-Criteria Decision Analysis (MCDA) methods in order to integrate stakeholder preferences and experts judgements into the analysis as well as for the calculation
of the total risk index. The RRA methodology is flexible and can be adapted to different
case studies (i.e. large rivers, alpine/mountain catchments, urban areas and coastal areas)
and spatial scales (i.e. from the large river to the urban scale). Overall, the methodology
intends to help decision-makers in examining the possible environmental risks associated
with uncertain future flood hazards and in identifying which prevention scenario could be
the most suitable one.
Keywords: Applications of MCDM, Environmental Decision Making, Risk Assessment.
70
TU4C (contributed)
A Multi-Criteria Aiding Tool for the Assessment of
French Statutory Urban Planning based on
ELECTRE TRI. A Case Study of the Local Plan of
Toulouse, France
Prevost, Aurélie (*) Molines, Nathalie
Université Technologie de Compiègne
Bandet, Jean
DGDUD / Toulouse Métropole
French legislation, according to European legislation (Directive 2001/42 CE), recently
provided an environmental assessment (including ex-post assessment) for some French
local plans. The ex-post assessment requires building a set of “environmental indicators”.
Nevertheless, those indicators don’t allow the planners to understand the traditional dynamics of the plan regulations, which are complex and can participate at the same time in
the implementation of “urban sustainability principles” lead by the legislation. The city
of Toulouse engaged a PhD research in order to propose methodological insights for the
evaluation of the impacts of the plan’s regulations. The research focuses on 3 main issues
of traditional French statutory planning: constructability, the relation between built and
unbuilt surfaces, and the amenities of each statutory zone. A set of indicators is created
for the different scales of the plot, the statutory zone and its geographical units. Later,
the indicators are used to highlight the existing links between the characteristics of the
territory and the regulation. After the creation of the indicators, the method proceeds in
two steps: 1. A comparison of each zone and deduction of the regulation’s effects, thanks
to the indicators. 2. Positioning of each zone according to the characteristics of the whole
territory. Here, we propose to classify each zone in accordance to different objectives to
achieve. This third step is divided into two aims: an ex-post assessment of the plan’s
effect on morphological criteria (the building morphology) and amenities in the zone, and
second, an ex-ante assessment to help planners find the adequate change for the definition
of zones and regulations. For this step, GIS software and the ELECTRE TRI method are
used. This paper proposes to present the second step of the method. After a synthetic
description of the project, we will focus on the implementation of the ELECTRE TRI
method, and the results obtained.
Keywords: Amenities, Characterization Of Statutory Zones, Constructability, Decision Support, ELECTRE TRI, Impacts Of Statutory Planning.
71
WE2C (contributed)
A Multi-Criteria Decision Analysis Methodology based
on Multi-Attribute Value Theory and Fuzzy Logic for
Quantitatively Scoring the Reliability of
Ecotoxicological Data
Isigonis, Panagiotis (*) Critto, Andrea Marcomini, Antonio
Semenzin, Elena Zabeo, Alex
Ca Foscari University of Venice
Ciffroy, Philippe
EDF
The advances in the field of ecotoxicology suggest exploring new ways of applying the current
methods for the evaluation of toxicity as well as identifying ways to make more efficient use of
the existing ecotoxicological datasets. In this scope and context, a new methodology is presented
which intends to assist the optimization of existing methods, by providing a tool for assessing the
relevance and reliability of ecotoxicological data for the definition of Species Sensitivity Distributions (SSDs), within the framework of ecological risk assessments (ERA). In order to estimate a
single aggregated reliability score for a given ecotoxicological datum, a ‘Multi-Criteria Decision
Analysis (MCDA)-based’ Weight of Evidence (WoE) methodology has been developed, including a hierarchical structure of 57 evaluation criteria, which was created based on the review of
the state of the art frameworks for the assessment of ecotoxicological data. The methodology is
based on the Multi-Attribute Value Theory (MAVT) and is able to integrate different types of
inputs. It incorporates the use of Fuzzy Logic operators for handling the inherent uncertainty,
which appears in the form of unreported information as well as possible lack of knowledge of the
experts. A panel of scientific experts on ecotoxicology was involved throughout the development
of the methodology for identifying, if any, the relations between criteria and evaluating the hierarchical structure to be used in the aggregation process. Expert Knowledge is incorporated
in the methodology and was extracted from the panel with the use of direct techniques, i.e.
questionnaires and dedicated meetings. The methodology is planned to be used and tested in
case studies, as part of the evaluation and follow up process of the research project “AMORE”
(Multi-Criteria Analysis for the Development of a Decision Support Tool for the prevention of
Environmental Risks), funded by the National French Research Academy (ANR).
Keywords: Fuzzy Logic, Group Decision Making, Multi-Attribute Value Theory, Multi-Criteria
Decision Analysis.
72
MO2G (invited)
A Multi-dimensional Corporate Social Performance
Versus a Multi-dimensional Corporate Social
Irresponsibility
Shalchian, Homayoon (*)
Laurentian University
El badraoui, Khalid
University of Rennes
Bouslah, Kais
Andrews University
Lilti, Jean-Jacques
University of Rennes
M’Zali, Bouchra
UQAM
This study investigates, on one hand, the relation between corporate social performance
(CSP) and corporate financial performance (CFP), and on the other hand, the relation between corporate social irresponsibility (CSI) and corporate financial performance. In our
previous study, assuming that investors consider and grant the same level of importance
to each and all dimensions of CSP, we examined the relation social-financial performance
using various performance models. Our previous study also showed that the nature of
activities of the firms can have a significant impact on the relation social-financial performance. Our first contribution in this study consists of analyzing the relation of different
dimensions of corporate social performance and financial performance separately in different industries. Our second contribution consists of analyzing the relation of corporate
social irresponsibility and financial performance in order to compare this relation with
social-financial performance. To do this we construct mutually exclusive portfolios based
on ’Strengths’ and ’Weakness’ rated by KLD. Our results show that in all industries
considered in this study and based on both strength and weaknesses rated by KLD, the
high ranked portfolios outperformed the low ranked portfolios. However, the gap between
portfolios performances is larger for the dimension ’environment’ in the industry where
the nature of activities corresponds to the industry ’Mining and chemicals’. Moreover,
our results show that in the industries ’Manufacturing’ and ’Services’ the dimensions ’Employees welfare’ constitutes a stronger signal relative to the other dimensions.
Keywords: Corporate Social Performance, Financial Performance, KLD Rating, Screening.
73
WE2D (invited)
A Multi-Objective Genetic Algorithm for Integrated
Scheduling of Production and Maintenance Activities
for a Single Machine
Ferreira, Rodrigo (*) Almeida-Filho, Adiel Cavalcante, Cristiano
Federal University of Pernambuco
This paper proposes a multi-objective model to deal with the integrated problem of
scheduling of production and maintenance activities in a single machine setting. Typically, the main issue to be addressed in scheduling problems is to define the sequence
in which production activities must be performed in order to maximize one objective
function. In addition to production activities, the model proposed in this paper considers the scheduling of maintenance activities in an integrated manner with two conflicting
objectives related to minimizing expected tardiness and maintenance costs. Since the
complexity of the solution space for this combinatorial problem is high, a multi-objective
genetic algorithm is proposed to find solutions that belong to the Pareto frontier, based
on the concept of dominance. The results emphasize that the model and algorithm can
provide non-dominated solutions promptly.
Keywords: Genetic Algorithms, Multi-Objective Optimization, Preventive Maintenance,
Production Scheduling.
74
MO2D (contributed)
A Multi-Objective Route Choice Model for
Health-Conscious Cyclists
Wang, Judith Y. T. (*)
University of Leeds
Cheung, Alan K. L.
Dirks, Kim N.
Ehrgott, Matthias
Pearce, Jon
Cyclists form the most vulnerable road user group in terms of injury from traffic accidents
as well as exposure to traffic-related air pollution. Ironically, commuter cyclists are often
motivated by improvement in health and fitness. Cycleways away from traffic with lower
concentrations of pollutants from motorised vehicles are often longer and hence require
longer travel times, while alternative routes sharing the road with other traffic, sometimes with buses, might result in exposure to higher pollutant concentrations. To help
commuter cyclists achieve their objectives of getting to work on time and maximising
their health benefits, we propose a multi-objective route choice model, for example, with
minimisation of travel time and pollutants uptake as the two objectives in a bi-objective
routing problem. A transport network information database is first constructed with
comprehensive information on link type, lane width, gradient, link average speed, traffic
volume, etc. such that both travel time and pollutants uptake can be estimated at a reasonably accurate level. In particular, the pollutants uptake will be dependent on exercise
level as well as the concentration of pollutants. Given an origin and a destination, to be
provided by a cyclist, we apply a multi-objective shortest path algorithm to determine
an efficient set of routes such that neither the total travel time nor the total pollutants
uptake can be reduced without worsening the other. Profiles of this route choice set in
terms of other useful information such as elevation and pollutant concentrations along the
route can also be provided. With our model, cyclists can more easily trade off between
time and pollutant uptake, and select a route with reasonable travel time at an appropriate exercise level with minimal negative impact on health. In cities with a hilly terrain
like Auckland, New Zealand, such information will be extremely valuable to current and
potential cyclists.
Keywords: Environment, Routing, Shortest Path, Transportation.
75
TU3D (contributed)
A Multi-objective Waste Collection Problem
´
Hernández-D´ıaz, Alfredo G. (*) Hinojosa, Miguel Angel
López-Sánchez, Ana Dolores
Pablo de Olavide University
Duarte, Abraham Gortázar, Francisco
Rey Juan Carlos
A new multi-objective GRASP algorithm has been implemented to solve a waste collection problem. It has been tested over a set of medium sized instances as well as the real
instance. The real world problem addressed is the waste collection problem in Seville
(Spain). Since vehicles can be only loaded from their right side, a directed graph with
two distinguished nodes namely the depot (where vehicles are stored) and the landfill
(where vehicles are emptied) has been considered. Each vehicle must visit streets until its
capacity is reached several times during the workday. Thus, each vehicle should perform
a route combining two or more trips. In the initial trip, vehicles start at the depot and
end at the landfill while the rest of the trips start and end in the landfill. The objectives
are minimizing the number of vehicles, the total distance and also the longest route with
the aim of balancing workloads. The research has been supported by the local public
cleaning firm of the city.
Keywords: GRASP Algorithm, Routing, Waste Collection Problem.
76
TH3G (invited)
A Multicriteria Decision Aiding Methodology to
Support Public Administration on Sustainable Energy
Action Plans
Norese, Maria Franca (*) Dall’O, Giuliano
Politecnico di Torino
Novello, Chiara
The Sustainable Energy Action Plan (SEAP) represents a strategic tool for achieving the
greenhouse gas reductions to 2020 for the municipalities that have joined the Covenant
of Mayors promoted by the European Commission. The intervention scenarios in relation
to the energy retrofits actions in the residential building stocks, that in small-medium
municipalities are responsible for more than 60% of CO2 emissions, are normally decided
on the basis of an economic analysis that calculates the ratio cost/performance. This
type of analysis, however, does not take into account important aspects for small and
medium-sized communities such as social aspects, environmental impacts, local economic
development and employment. A more comprehensive and effective tool to support the
choices of public administrators could be a multiple criteria model. A methodology has
been developed to support Public Administration on programming Sustainable Energy
Action Plans with a more targeted approach to sustainability. The methodology, that
includes the ELECTRE III method, was applied to a medium-size Municipality in Lombardy region (Italy) and the results were compared with the classical economical approach
and analyzed in terms of model consistency and adequacy of the methodology to the actual processes of policymaking, planning and action implementation. Difficulties and
opportunities that characterize the modeling process and the application of an ELECTRE method in this context will be described in relation to the impacts on the decision
processes and the different possible decision aiding interventions.
Keywords: ELECTRE Methods, Multi-Criteria Decision Aid, Public Policy, Strategic
Planning, Sustainability.
77
TU3C (contributed)
A Multicriteria Optimization Model for a
Humanitarian Logistics Problem: An Integral
Approach
Gaytán-Iniestra, Juan (*)
Universidad Autónoma de México
Mej´ıa-Argueta, Christopher
ITESM Campus Toluca
Vitoriano, Bego˜
na
Universidad Complutense de Madrid
Disasters are phenomena which strike countries around the world. This work introduces
an integral optimization model to prepare: Distribution, evacuation, location of facilities
and meeting points, as well as a stock management policy during floods with multicriteria
(total cost, as well as equity minimizing the maximum evacuation and distribution flowtimes). The efficient frontier is built through the weighthing and the epsilon-constraint
methods through the use of commercial software. The usefulness and the robustness of
the model is validated through numerical experiments on 11 different instances including
a mexican case study.
Keywords: Applications of MCDM, Case Study, Humanitarian Logistics, Multi-Objective
Optimization, Pareto Optimal Solutions, Sensitivity Analysis.
78
FR3E (contributed)
A Multicriteria Prediction Model for Project
Classifications
Laryea, Rueben (*)
Stockholm University
Project distress predictions are essential in project management. Developing appropriate methods to classify projects and building prediction models for Multi-Criteria decisions requires empirical methods to minimize misclassification errors. This paper carries
out Multi-criteria analysis to classify projects using a preference disaggregation method,
UTADIS. The UTADIS requires a predefined classification which is implemented using
critical path analysis. The methods are applied on three projects and results in a no
misclassification error and an effective prediction model.
Keywords: Classification, Critical Paths, Multi-Criteria Decision Model, Preference Disaggregation, Project Risk.
79
TH2F (contributed)
A Multiple Objective View on Robust Optimization
and Stochastic Programming with Finite Scenario Sets
Klamroth, Kathrin (*)
University of Wuppertal
Köbis, Elisabeth
University Halle-Wittenberg
Schöbel, Anita
Georg-August University Göttingen
Tammer, Christiane
University Halle-Wittenberg
Many real world optimization problems are contaminated with uncertain data. One way
of dealing with such optimization problems is described in the concept of robustness:
Instead of assuming that all data are known, one allows different scenarios for the input parameters and looks for a solution that works well in every uncertain scenario.
Another prominent way of dealing with uncertain optimization is the field of stochastic
programming. Different from robust optimization, stochastic programming assumes some
knowledge about the probability distribution of the uncertain data. The objective usually
is to find a feasible solution (or a solution that is feasible with a certain probability) that
optimizes the expected value of some objective or cost function. In this talk, we will link
different concepts of robustness and of stochastic programming that usually have been
considered fundamentally different through the formulation of deterministic multiple objective counterparts, where objective functions are defined by different scenarios of the
uncertain model. Assuming that the set of scenarios is finite, we will show that many
uncertain optimization problems have a deterministic counterpart in this framework. Similarly, we show that many different concepts of robustness and of stochastic programming
can be described as special cases of a general nonlinear scalarization method by choosing
the involved parameters and sets appropriately. This forms the basis for an analysis of the
interrelations and also the differences between established concepts in robust optimization
and stochastic programming. By providing additional trade-off information between alternative efficient solutions, the multiple objective counterpart can facilitate the decision
making process when deciding for a most preferred robust solution.
Keywords: Non-Linear Scalarization, Robust Optimization, Stochastic Programming,
Uncertainty.
80
TU4F (contributed)
A Neural Network based Hybrid Evolutionary
Multiobjective Optimization Algorithm for
Computationally Demanding Problems
Hakanen, Jussi (*)
Kokko, Tommi
Sindhya, Karthik
University of Jyväskylä
Solving real-world multiobjective optimization problems (MOPs) is a computationally
demanding task due to, e.g., more accurate and complex simulation models used and
the number of objective functions considered. One approach to tackle this challenge is
to utilize computationally inexpensive surrogate functions to approximate the objective
functions. Hybrid evolutionary multiobjective optimization (EMO) methods are also used
to handle computationally demanding MOPs. Here a global search (EMO method) and a
local search are combined to improve the performance of EMO methods. In local search
we optimize an achievement scalarizing function using a suitable optimization method
and is sparsely applied on selected solutions. In this research, we utilize the benefits of
surrogate functions and hybrid EMO methods. Here, we consider neural networks (NNs)
as an example of surrogate functions and use them to approximate the objective functions. The surrogate functions are built using a small set of sample points evaluated with
the original computationally costly model. We combine a hybrid EMO method with a
NN in a way that the objective function evaluations of EMO part are replaced with a
NN and the objective function evaluations of the local search are performed with the
original model. This leads to a significant saving in the computation time since most
of the evaluations of hybrid EMO methods are used in the EMO part. Here we consider multilayer perceptron and a radial basis network as examples of NNs. We test the
efficacy of our combined approach using a computationally expensive engineering problem.
Keywords: Computational Cost, Evolutionary Multi-Objective Optimization, Hybrid
Approaches, Surrogate Modeling.
81
TH3D (contributed)
A New Dominance Measuring Method for MCDM
with Ordinal Information About DM’s Preferences
Aguayo, Ernesto (*)
Jiménez, Antonio
Mateos, Alfonso
Different methods based on dominance intensity measures have been recently proposed by
different authors to deal with imprecision concerning MCDM problems. These methods
compute dominating and dominated measures on the basis of the pairwise dominance
values, which are then used in different ways to derive dominance intensity values on
which the ranking of alternatives is based. In this paper we propose an extension of a
dominance measuring method previously published by the authors to manage MCDM
problems in which the decision maker’ preferences are represented by ordinal information, i.e a ranking of criteria is provided based on their relative importance, whereas
alternatives are also ranked in each criterion. First, optimization problems are solved
to derive pairwise dominance values using the centroid function to represent the relative
importance of criteria, while ordinal information about alternative in each attribute is
incorporated as constraints. Once the dominance matrix is computed, we ponder the
elements of the dominance matrix with the distance that exists between the mean weight
vector, which is the result of each mean value from the end points of the polytope that
represents the imprecision of the problem, and the optimal weight vector, that is the result of the optimization model for that element. Finally, a dominance intensity measure
is computed from the pondered dominance matrix on which the ranking of alternatives
is based. Monte Carlo Simulation techniques are used to compare the performance of
the extension we proposed with other dominance measuring methods, with an approach
proposed by Sarabando and Dias for MCDM problems with ordinal information. Two
measures of efficacy are considered, the proportion of all cases in which the method selects the same best alternative as in the TRUE ranking (hit ratio) and how similar the
overall alternative-ranking structures are in the TRUE and the method-driven rankings
(rank-order correlation).
Keywords: Dominance Measuring Methods, Imprecise Information, Monte Carlo Simulation, Multi-Criteria Decision Making.
82
TU3F (invited)
A New Evolutionary Algorithm for the Bi-objective
Ring Star Problem
Galé, Carmen (*)
Calvete, Herminia I.
Iranzo, José A.
Universidad de Zaragoza
The ring star problem aims to locate a cycle through a subset of nodes of a graph and
assign each non-visited node to a visited one. The goal is to minimize the ring costs
and the assignment costs. The bi-objective ring star problem (B-RSP) arises when both
objectives are considered individually instead of jointly. In this paper, a new evolutionary algorithm is proposed to approximate the set of Pareto optimal solutions. Previous
evolutionary algorithms for solving the B-RSP use the random keys encoding mechanism,
which easily allows to applying evolutionary operators. With this encoding at hand, the
chromosome contains all the information about the cycle. Hence, in order to evaluate the
fitness of a chromosome, the algorithm directly computes the cost of the edges in the cycle
and the cost of the assignments. The distinctive aspect of the proposed algorithm is that
the chromosome only contains information on the nodes in the cycle, but not on their
position. Hence, we encode each chromosome as a binary vector of dimension the number
of nodes in the network, where 1 indicates that the node is in the cycle. After knowing the
nodes in the cycle, the complete description of the cycle is obtained by solving a TSP on
these nodes. The algorithm proceeds by applying uniform crossover operator, mutation
and local search. In spite of the more complex process to compute the cycle, the algorithm has shown its efficiency in terms of the quality of the Pareto frontier approximation
provided and the computing time when applied to a set of benchmark problems.
Keywords: Bi-Objective Ring Star Problem, Evolutionary Algorithm.
83
FR3C (contributed)
A New Exact Method for Optimizing a Linear
Function over an Integer Efficient Set
Chaabane, Djamal (*)
USTHB
During these two decades, researchers and practitioners have been increasingly interested
by the problem of optimizing a linear function over the efficient set of multiple objective linear programming problem . Several methods and algorithmic ideas have been
developed, in general, these approaches can be classified and grouped according to the
methodological concepts. Among others, adjacent vertex search technique, face search
algorithm, algorithms include Branch and bound, Lagrangian relaxation methods, dual
approach and other methods. An overview of these approaches can be found in Yamamoto
(2002). Contrary to the continuous case, few exact algorithms have been suggested for
solving the problem involving discrete decision variables. The first attempt for optimizing
over the integer efficient set is due to Nguyen 1992), where only an upper bound value
for the main objective is proposed. Thefirst exact method was developed by Chaabane
and Abbas (2006) based on a simple selection technique that improves the main objective
value at each iteration. Two types of cuts are used and performed successively until the
optimal value is obtained and the current truncated region contains no integer feasible
solution. In the present paper, we propose a new exact algorithm, using an augmented
weighted Tchebychev norm, combined with a reduction process of the admissible region
by the successive addition of constraints. Sub-programs that were integrated in the above
cited methods are avoided and the technique produces two outputs : an optimal efficient
solution and a sub-set of efficient solutions.
Keywords: Efficiency, Integer Linear Programming, Multi-Criteria Decision Analysis,
Multi-Objective Optimization.
84
MO4D (invited)
A Parameterized Approximation for Matroids with
Multiple Objectives
Tlilane, Lydia (*) Gourvès, Laurent
Monnot, Jérôme
Université Paris Dauphine
We consider an optimization problem on a matroid with multiple objectives. One has
to find a base that maximizes n additive functions. Taken separately, every objective
can be optimized by applying the well known greedy algorithm (also known as Kruskal’s
algorithm for maximum weight spanning trees). However, the existence of a feasible solution that maximizes simultaneously all objectives is unlikely. In addition, the existence
of a solution that maximizes the function of the least satisfied objective has been shown
NP-complete in [E. Ehrgott, On matroids with multiple objectives. Optimization 38.1
(1996): 73-84]. Thus, in order to obtain a satisfactory solution in polynomial time, we
seek for an approximation of the ideal point. A feasible solution (base) B is a (e1 , ..., en )approximation of the ideal point if the ratio between the ith value of B and the optimum
on objective i is at least ei for all i = 1, . . . , n. There exist conflictual instances which
are unbalanced, i.e., every feasible solution is close to optimality for one objective, and
very far from optimality for another. In this case, we can not guarantee anything. If
the matroid is simple (every element or couple of elements is independent), there exists a
(1/2, 1/3)-approximation for n = 2 objectives which is tight [L. Gourvès, J. Monnot and
L. Tlilane. Approximate Tradeoffs on Matroids, ECAI 2012: 360-365]. However, if n ≥ 3,
then the problem is not constant-approximate even if the matroid is simple. To go further
and circumvent this negative situation, we resort to a set of parameters a1 , . . . , an where
ai is the maximum value for an element of the matroid on objective i. Our contribution
is an approximation depending on ai defined as a family of functions fn (ai ). We propose
a polynomial time algorithm providing a (fn (a1 ), ..., fn (an ))-approximate base, according
to the possible values of ai .
Keywords: Approximation of The Ideal Point, Matroids.
85
TU5E (invited)
A Performance Monitoring System based on a
Combined MCDM Methodology
Kandakoglu, Ahmet (*)
Kandakoglu, Makbule
Turkish Petroleum Corporation
This study proposes a performance monitoring system based on the combined utilization
of Analytic Hierarchy Process (AHP), Weighted Product (WP), Utility Theory (UT) and
TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) methods. In
the proposed system, the AHP is applied to identify the key performance criteria, structure the criteria hierarchy and determine the weights of the criteria from the subjective
judgments of experts. UT is used to construct the utility functions for each criterion
using the organizational standards. WP method is then utilized to calculate the final
performance values by penalizing the poor criteria measurements. TOPSIS method is
applied to make an evaluation with respect to the standards. Finally, a case study is presented to illustrate the potential and applicability of the proposed system for evaluating
the performances of departments and district managements in Turkish Petroleum Corporation. The results show that building this kind of systems enables the aggregation the
big data stored in information systems to support the decision making processes within
the organization and also allows the decision-makers manage this business processes in
an effective and timely manner.
Keywords: Analytic Hierarchy Process, Multi-Criteria Decision Analysis, Performance
Measurement, TOPSIS, Weighted product.
86
FR2G (contributed)
A Preference-Based Facility Location Problem
Krupi´
nska, Katarzyna (*)
Wroclaw University of Economics
We consider the problem of locating facilities on a directed graph. Facilities are allowed
to be sit at the vertices of the graph. There is also defined a binary relation on the power
set of the set of arcs, according to which preferred paths are determined. In an allocation
phase, clients are assigned to facilities based on preferred paths, while in a location phase
different location patterns are compared using another preference relation defined on the
power set of the set of paths. We formalize the solution concept and present conditions
on preference relations under which a prespecified solution may be obtained.
Keywords: Location, Preferences.
87
MO4D (invited)
A Primal-Dual Approach for Optimizing Ordered
Weighted Average in Perfect Matching Problems
Weng, Paul (*)
LIP6 - UPMC
Nguyen, Viet Hung
LIP6 - UPMC
A perfect matching [Edmonds, 1965] for a (non necessarily bipartite) graph is a subset of
non-adjacent edges that cover every vertex. In this work, we assume the graph is weighted
and our aim is to find a perfect matching whose edge weights are fairly distributed. This
issue is important for instance if one wants to be fair to the pairs of nodes in a matching.
The standard formulation of the weighted perfect matching problem, which consists in
finding a perfect matching that optimizes the sum of the edge weights, does not allow any
control on the distribution of the edge weights. To model fairness, we use the Ordered
Weighted Average (OWA) with decreasing OWA weights [Weymark, 1981], instead of the
sum, to aggregate the edge weights. While OWA is a non-linear aggregating function, a
0,1-linear program can be formulated for finding an OWA optimal perfect matching, exploiting a linearization of OWA proposed by Ogryczak and Sliwinski (2003). For solving
this problem, we propose an approximation scheme with a guaranteed performance based
on a primal-dual approach.
Keywords: Matching, Multi-Objective Optimization, Ordered Weighted Av.
88
WE1G (contributed)
A Semidefinite Programming Approach for Solving
Multiobjective Linear Programming
Blanco, Victor (*)
Universidad de Granada
El-Haj Ben-Ali, Safae
Universidad de Sevilla
Puerto, Justo
Several algorithms are available in the literature for finding the entire set of Pareto-optimal
solutions of MultiObjective Linear Programming (MOLP). However, all of them are based
on active-set methods (simplex-like approaches). We present a different method, based on
a transformation of any MOLP into a unique lifted SemiDefinite Program (SDP), the solutions of which encode the entire set of Pareto-optimal extreme point solutions of MOLP.
This SDP problem can be solved, among other algorithms, by interior point methods;
thus our approach provides a new non-active-set method to find the set of Pareto-optimal
solutions of MOLP.
Keywords: Linear Programming, Multi-Objective Optimization, Semidefinite Programming.
89
FR3B (contributed)
A Sequential Goal Programming Model for University
Timetable Construction
Skocdopolova, Veronika (*)
University of Economics, Prague
Goal programming is a modelling technique usually used for multi-criteria decision making. Timetabling at universities, formulated as an integer or binary programming model,
is a problem that belongs to difficult problems. There are two basic approaches for solving the timetabling problem. The first is to solve one complex model usually via integer
programming, where often various heuristic and metaheuristic methods (because of computational difficulty) are used. The other approach consists in decomposing the problem
into interrelated stages. This paper presents a sequential model, which uses goal programming for solving the timetabling problem in all stages. The model is inspired by model
applied by Al-Husain et al. (2011) on scheduling problem at Kuwait University, College
of Business Administration. Al-Husain et al. decompose the timetabling problem into
three interrelated parts, where each stage is optimally solved and the outputs are used as
input in the next stage. At first teachers are assigned to courses, then courses are assigned
to time slots, and finally time slots are assigned to classrooms. This approach enables
solving the timetabling problem in a reasonable time. The presented model is applied to
timetable construction of department of econometrics at University of Economics, Prague.
Some new requirements are added contrary to Al-Husain’s model. E.g. there are some
courses that have to be in computer classrooms, some of them have to be in a concrete
room; some pairs of courses have to be taught simultaneously – one in non-computer
classroom, the other one in computer classroom, students rotate every week from one
room to the other one, because students do not need PC every lesson of the semester and
there are not enough disposable computer classrooms for the department. The aim of the
model is to satisfy teacher’s preferences of teaching time. The research is supported by
the Internal Grant Agency of the University of Economics (grant no. F4/11/2013).
Keywords: Applications of MCDM, Goal Programming, Timetabling.
90
MO4F (contributed)
A Stakeholder based Multi-Criteria Evaluation
Framework for City Distribution
Milan, Lauriane (*) Macharis, Cathy
Verlinde, Sara
Vrije Universiteit Brussel
Urban areas are facing several challenges, both in terms of logistical performance and
environmental impact. One of them is how to organise freight transport in a sustainable way. Many innovative city distribution concepts that have been tested fail because
not all stakeholders were taken into account in the decision-making process (Macharis &
Melo, 2010). Evaluating urban freight solutions in order to assess if they stand a chance,
summons the need for a new approach taking into consideration different conflicting objectives from different stakeholders. The Multi-Actor Multi-Criteria Analysis (MAMCA)
developed by Macharis (Macharis, 2000) combines the conventional multi criteria decision
analysis (MCDA) with the notion of stakeholders in an explicit way. Within Straightsol
(Strategies and measures for smarter urban freight solutions, EC FP7), a systematic impact assessment framework dedicated to freight transportation has been developed. The
so-called City Distribution–Multi Actor Multi Criteria Analysis (CD-MAMCA) incorporates the city distribution actors and their objectives as the primary focus complemented
with a MCDA. In view to compare the effectiveness of the different alternatives, the
PROMETHEE GAIA Multi-criteria Analysis method was chosen. In this paper the specific framework dedicated to city distribution is explained with its applications to a case
study within the Straightsol project. The relevant stakeholders within urban and interurban freight transport context are shown together with their important criteria. Besides the
benefits obtained from the well-structured decision approach that offers the CD-MAMCA
(Macharis et al, 2012) the results of the MCA reveal the preference order of the alternatives and allow developing specific recommendations for future freight measures. The
same approach will be executed during the Straightsol project for the evaluation of seven
demonstrations and aims to become an integrated framework for any evaluation.
Keywords: CD-MAMCA, City Distribution, Multi-Actor Multi-Criteria Analysis, PROMETHEE.
91
TU4D (contributed)
A Stochastic Choquet Integral Preference Model:
SMAA-Choquet
Corrente, Salvatore (*)
Angilella, Silvia
Greco, Salvatore
University of Catania
The Choquet integral is an aggregation function used in Multicriteria Decision Aiding
(MCDA) to deal with interaction of criteria. The Stochastic Multiobjective Acceptability Analysis (SMAA) is an MCDA methodology used to deal with imprecision or lack
of data in the decision problem at hand. In this paper we propose to integrate SMAA
methodology with the Choquet integral preference model in order to obtain robust results
according to available information. We show also how SMAA permits to induce not only
a capacity representing importance and interaction between criteria, but also the common
scale required by the Choquet integral.
Keywords: Choquet Integral, Decision Making, Imprecise Information, Linear Programming, SMAA.
92
TU3B (invited)
A Strategic Information System for Sustainable
Supply Chain
Taraszewski, Steve (*)
Karpak, Birsen
Sustainable supply chains (SC) meet the needs of the present generation without compromising the ability of future generations to meet their own needs. There are three
pillars of Sustainability: Economic, Environmental, and Social. The Economic pillar is
the most common dimension addressed. Environmental life cycle impacts are increasingly
being studied. However, research is still dominated by green/environmental issues and
the social aspects and integration of the three dimensions are still rare. It is worth noting that no study has comprehensively addressed the three dimensions of sustainability
(economy, society and environment), triple bottom line (TPL). There is an information
flow component of supply chain management (SCM) systems. We contend that SCM
systems are influenced by an information/knowledge sharing culture - the ability and
willingness to share information and knowledge as to best-practices or potential opportunities or threats. Information systems (IS) ’touch’ a SC by facilitating the efficient and
effective flow of data and information for elevating business processes supporting the entire SC. Also, IS offer the possibility of automating/informating/transforming the SC and
its constituents. Few studies of sustainability incorporate the IS perspective. We propose
a new strategic information system for sustainable supply chains. We have applied the
proposed framework to an illustrative problem. We are evaluating three strategic partners
with respect to TPL. Initial results illustrated that: Economic pillar is the most important followed by environmental and social performance dimensions; Partner C is the best
strategic partner; innovation is the most influential performance factor followed by cost;
among environmentally influential organizational practices recycle and reduce turned out
the most influential; health care and labor equity are the most influential social factors,
and cultural diversity is next.
Keywords: Analytic Network Process, Strategic Information Systems, Supply Chain
Management, Sustainability.
93
TH4B (contributed)
A System Approach for Green Supplier Selection
Based on the ANP Model with BOCR for the
Electronics Industry in Taiwan
Shih, Hsu-Shih (*)
Chen, Chiau-Ching
Lin, Yi-Chun
Tamkang University
This study deals with the problem of green supplier selection for the electronics industry
in Taiwan. After defining a problem-solving procedure, we use an analytic network process (ANP) model with benefits, opportunities, costs, and risks (BOCR) merits to assess
all factors for ranking suppliers. This study then illustrates a real-world case of supplier
selection for the adapter of one type of notebook PC. We also set up a radar chart to
show the priorities of the factors under the four merits so that managerial implications
can be drawn. Firms in the past mostly relied on tangible criteria for making purchasing
decision instead of considering the uncertain opportunities and risks in the evaluating
process. The ANP model with BOCR merits can incorporate the uncertainties into the
model. First, we compile 12 general criteria and 6 green criteria on supplier selection.
Second, we take on the content validity ratio and factor analysis for acquiring elements
and clusters under the BOCR aspects and then apply the ANP approach to process the
independence and feedback among elements and clusters. Third, we execute a sensitive
analysis. Lastly, we present the priorities of all factors under different merits. The model
simultaneously considers the general criteria and green criteria in order to reward the
environmental efforts of the suppliers. The results show that valued clusters are slightly
different from the aspects of BOCR, i.e., “production adaptability”, “promise to customer”, “price” and “product reliability” are the major concerns, whereas green criteria
are less valued. In addition, the ANP with BOCR can describe the criteria and factors
in details and also consider the effects of uncertainties for a suitable decision. In the final
part, we draw up suggestions for the stakeholders in the hope that they put forth more
efforts on environmental protection in the future.
Keywords: Analytic Network Process, Case Study, Environmental Decision Making,
Sensitivity Analysis.
94
TH3F (contributed)
A Tabu Search Algorithm for Multi Objective Open
Vehicle Routing Problem
Ozcetin, Erdener (*) Ozturk, Gurkan
Anadolu University
The Open Vehicle Routing Problem (OVRP) is a form of Capacitated Vehicle Routing
Problem that turning depot from last node of route is not considered. The OVRP is
NP-Hard and finding optimal solution with exact methods is extremely hard. In this
study, we consider the OVRP as a multi-objective problem in which the objectives are
minimizing total cost defined as a function of distance and maximizing the service level
for customers. In order to scalarize the problem, conic scalarization which is powerful
method to find pareto solutions is used instead of linear scalarization. For the real life
case problem a variant of Tabu Search algorithm is developed. Obtained results show
that proposed algorithm works efficiently in a short time period without ignoring service
level.
Keywords: Combinatorial Optimization, Tabu Search, Vehicle Routing Problem.
95
TH4B (invited)
A Three-Stage Methodology to Analyze the Global
Competitiveness of a Sector
Ozaydin, Ozay (*)
Cekyay, Bora Onsel Ekici, Sule
Dogus University
Ulengin, Fusun
Kabak, Ozgur
This study focuses on developing a methodology to identify key success factors for improving the global competitiveness of a given sector, and to determine how much each
critical factor should be ameliorated in order to achieve a specific competitiveness level
with the minimum effort. In the first step, Analytical Hierarchy Process (AHP) is used
to prioritize the most necessary sub-goals to maintain the competitiveness of the sector,
which are determined by the main stakeholders of the sector. The second step attempts to
quantify the sub-goals prioritized in the first step. This is achieved by the selection of the
most relevant factors, included in the World Economic Forum (WEF) Competitiveness
Index, for measuring the development of the corresponding sector in a given country. In
the last step, a goal programming model is formulated to determine the minimum level
of improvements in the selected WEF factors necessary to achieve a specific competitiveness level. A case study for Turkish Machinery Sector is also presented to illustrate the
applicability of the proposed methodology.
Keywords: Analytic Hierarchy Process, Competitiveness, Machinery Industry.
96
TH2B (contributed)
A Two Objective Classification Approach based on
Conic Functions
Ozturk, Gurkan (*)
Anadolu University
Kasimbeyli, Refail
Anadolu University
Polyhedral conic functions were defined as a new class of functions for classification problems. The graph of a PCF is a cone and its level set at zero which is a convex polyhedron
divides the whole space into two parts:inside and outside. A PCF is defined as a linear
functions extended with l1 norm and characterised of its center point. If the center point
is known in advance the construction of a function is straightforward, by solving a linear
programming problem a PCF can be obtained easily. In this study a two phased approach
is proposed. In the first phase according to different center points and norm term several
conic functions are constructed then in the second phase a two-objective integer programming model is solved to find classification function. The performance of the proposed
algorithm is shown on the literature test problems.
Keywords: Classification, Data Mining, Mathematical programming.
97
WE1G (contributed)
About an Implementation of a 3-Objective Linear
Programming Solver
Schenker, Sebastian (*)
Zuse institute Berlin
Despite the growing interest in multi-objective optimization and the wide availability of
commercial and non-commercial single-objective solvers there exist only very few multiobjective (linear) programming solvers available to the scientific community. In this talk
I want to present an implementation of a 3-objective linear programming solver that
computes all extreme non-dominated points (and the corresponding weight space decomposition). In every iteration a weight is computed that leads to an unexplored extreme
non-dominated point based on the approach of Benson and Sun. However, in contrast to
the latter method we take the weight space decomposition into account in order to avoid a
consecutively increasing linear system used for computing new weights. Instead, we look
for new weights only on the weight space edges and keep the weight space decomposition
updated after every iteration. The solver uses the lp interface of SCIP (framework developed at Zuse institute) that allows to connect different single-objective lp solvers (like
soplex, cplex or gurobi) for the computation of the scalarized problem. The first part
of the talk is concerned with a worst-case example based on deformed cubes that shows
that we cannot expect a small number of extreme non-dominated points even for a fixed
number of objectives. In the second part the basic ideas behind the solver and the solver
itself are presented.
Keywords: 3-Objective Linear Programming Solver, Linear Programming, Multi-Criteria
Decision Analysis, Multi-Objective Optimization, Software.
98
MO2F (invited)
Adaptive Guided Evolutionary Multi-Objective
Optimization
Siegmund, Florian (*)
University of Skövde
Deb, Kalyanmoy
Michigan State University
Ng, Amos
In Multi-objective Optimization many solutions have to be evaluated in order to provide
the decision maker with a diverse Pareto-front. In Simulation-based Optimization the
number of optimization function evaluations is very limited. If preference information
is available however, the available function evaluations can be used more effectively by
guiding the optimization towards interesting, preferred regions. One such algorithm for
guided search is the Reference-point guided NSGA-II. It takes reference points provided
by the decision maker and guides the optimization towards areas of the Pareto-front close
to the reference points. We propose several extensions of R-NSGA-II. In the beginning
of the optimization runtime the population is spread-out in the objective space while
towards the end of the runtime most solutions are close to reference points. The purpose of a large population is to avoid local optima and to explore the search space which
is less important when the algorithm has converged to the reference points. Therefore,
we reduce the population size towards the end of the runtime. R-NSGA-II controls the
objective space diversity through the epsilon parameter. We reduce the diversity in the
population as it approaches the reference points. In a previous study we showed that
R-NSGA-II keeps a high diversity until late in the optimization run which is caused by
the Pareto-fitness. This slows down the progress towards the reference points. We constrain the Pareto-fitness to force a faster convergence. For the same reason an approach
is presented that delays the use of the Pareto-fitness: Initially, the fitness is based only on
reference point distance and diversity. Later, when the population has converged towards
the Pareto-front, Pareto-fitness is considered as primary-, and distance as secondary fitness. We combine the proposed approaches and evaluate them on benchmark functions
and in a case study of an industrial production line for car manufacturing.
Keywords: Adaptive Strategies, Evolutionary Multi-Objective Optimization, guided
search, Reference Point, Simulation-Based Optimization.
99
MO5E (award)
Advanced Metaheuristics for Multi-objective
Optimization: Design, Analysis and Application
Durillo, Juan J. (*)
University of Innsbruck
The optimization of problems involving two or more conflicting criteria consists of two
phases: to compute the Pareto front-solving-, and to select a subset of points from it
according to user preferences-decision making-. Computing the Pareto optimal front is
impractical in most problems because of several reasons: it may consist of infinite points
and it can be computationally expensive. On account of this, approximations to the
optimal Pareto front are used. From a decision making point of view, the interest is to
obtain approximations that are useful enough to choose satisfactory solutions. To this
end, not all approximations are valid but those with a good convergence and diversity are
seek. Convergence reflects optimality: the closer to the optimal Pareto front the better.
Diversity has to do with the fact that the approximation consists of a finite number of
points. The better these points cover the optimal Pareto front, and the better they are
spread alongside it, the higher the success. An approximation with these two properties is
then an important tool from which a decision maker may benefit, revealing points where
the trade-off among the criteria is the most attractive. Therefore, the success of solving a
given multi-objective optimization problem is strongly tied to the triumph of this phase.
In addition, the time for solving problems still remains a wall to bring down specially
for real-world problems that rely on expensive simulations and non-linear formulations.
Researches are then challenged to design advanced methods able of computing these high
quality Pareto fronts while reducing the required time to a solution. Two non-orthogonal
ways to this end exist: reducing the number of required evaluations without damaging the
quality of the computed results and applying parallelism. This talk aims to describe some
advanced parallel metaheuristic methods targeted to face the aforementioned challenges
and their application to solve real-world problems.
Keywords: Applications, Metaheuristics, Multi-Objective Optimization, Parallelism.
100
MO4G (contributed)
Adversarial Risk Analysis on Transport Infrastructures
Cano, Javier (*)
Rey Juan Carlos University
R´ıos Insua, David
Universidad Rey Juan Carlos
Tedeschi, Alessandra
DeepBlue Srl
Turhan, Ugur
Anadolu University
In this work we analyze how the authorities of a small airport are concerned with terrorist threats against the airport ATC Tower, aimed at taking hold of air traffic controllers
before or during flight control operations. Consequences of this could be severe: (1) crisis
for air traffic operations in the air field and airspace; (2) flight safety negatively affected;
(3) air traffics cancelled or diverted to other ATC unit or air fields, with enormous economic, social (in extreme cases, even in terms of human lives) and image costs. As a way
to mitigate the impact of such menace, preventive measures, such as security checks and
surveillance cameras, are being implemented. They have considerable associated costs
but, by deploying them, authorities expect to deter the actions of the terrorists. The
multiobjective nature of the problem becomes a more delicate issue as human lives and
high costs are concerned. We model the problem as a particular case of a Sequential
Defend-Attack model within the Adversarial Risk Analysis framework, in which authorities (the Defender) would deploy a portfolio of preventive measures. Then, the terrorists
(an intelligent Attacker), having observed them, would follow a given terrorist strategy.
We shall assume that the consequences for the Defender will depend on the effort in implementing their protective actions and on the mitigated result of the terrorist attacks.
Similarly, the consequences for the Attacker will depend on the effort in developing their
terrorist actions and their result. Both the Defender and the Attacker are regarded as
expected utility maximizers. We present numerical results, giving advice for devising a
multiobjective security contingency plan. The model is open to extensions, as e.g. larger
installations, with more than one critical installations to be defended, new threats (more
than one intelligent attacker), or additional countermeasures deployed by different agents
(more than one defender).
Keywords: Adversarial Risk Analysis, Airport Security, Cost Utility Analysis, Risk
Analysis, Utility Theory.
101
TH2E (invited)
Agent Assisted Interactive Multiobjective
Optimization for Computationally Demanding
Problems
Ojalehto, Vesa (*)
Miettinen, Kaisa
University of Jyvaskyla
Podkopaev, Dmitry
A computationally expensive multiobjective optimization problem can be approached by
replacing it with a computationally inexpensive surrogate problem, which approximates
the set of Pareto optimal solutions of the original problem. This replacement enables applying interactive solution methods on account of faster interaction between the decision
maker (DM) and the solution method. Decreasing computing time, however, is achieved
at the expense of reducing the precision. Besides that, such an approach does not eliminate the cognitive difficulties experienced by the DM in problems with high dimensions
of objective and design spaces. In order to overcome mentioned difficulties, we introduce
multiple independent agents which provide the DM with information on the accuracy of
solution candidates, learn DM’s preferences and based on that, improve the accuracy of
the surrogate problem in those areas of the decision space which may be interesting for
the DM. Furthermore, the agents can assist the DM by generating new Pareto optimal
solution candidates based on the learned preferences. We demonstrate the agent assisted
interactive decision making approach by applying the interactive NIMBUS method to an
example problem of process engineering based on simulation of a generic two-stage separation process using the APROS dynamic plant-wide process simulation software.
Keywords: Computational Cost, Interactive Methods, Multi-Agent Systems, NIMBUS,
Preference Learning.
102
WE1C (contributed)
An Algorithm to Determine Efficiency Scores in Large
DEA Data Sets
Yildiz, Gazi Bilal (*)
Erciyes University
Soylu, Banu
Erciyes University
When there are huge number of Decision Making Units (DMUs) and large number of
input/output, identifying efficient units via a DEA model could be computationally complex. In this study, we propose a new algorithm to determine efficient DMUs and to
estimate efficiency scores of inefficient units. Initially, we partition the original problem
into sub-problems and solve these sub-problems to construct a set of initial efficient units.
This set leads us to estimate the efficiency scores of other units. We apply the algorithm
over large data sets and compare the performance with classic DEA and a decomposition
algorithm from the literature. We present our results.
Keywords: Data Envelopment Analysis, Multi-Criteria Decision Making.
103
TH4E (contributed)
An Assessment of Multipurpose Water Resources
Using Multicriteria Analysis
Ribas, Jose Roberto (*) Pinheiro, Mariana
Universidade Federal do Rio de Janeiro
Severo, Juliana Ribas
Delta Energia
Water is essential for life; therefore, its management has attracted the attention of researchers worldwide. When considering large reservoirs formed by hydroelectric projects,
the simultaneous use of water is a natural way to attend different needs of the local population, which, in some cases, are conflicting. The consideration of one specific use of water is
a collective concern; consequently, its importance must be evaluated on a comparative basis, weighted by a set of relevant benefits provided to the ecosystem. This study describes
the combination of two qualitative techniques (in-depth interview and content analysis)
with the FAHP method, on attempting to rank a set of alternatives of a multipurpose
water resource. A study was made with the reservoir of Corumba hydroelectric power
plant, located in the Central region of Brazil, which borders eight municipalities. The
multicriteria model was specified with the assistance of 43 experts classified among four
groups (power plant owners; federal agencies experts; municipal mayors and officers; local
population representatives). Initially, the qualitative assessment indicated eleven benefits
required by the ecosystem and ten possible uses for the reservoir (electricity generation,
water supply and irrigation were excluded), too complex to be treated by a multicriteria
technique. The dimension of the model was reduced by means of a statistical cut off,
resulting in six benefits and five uses. The metrics were done through a pairwise comparison using a nine-point Saaty’s scale. The FAHP technique estimated the weights,
which resulted in four different ranks of benefits and uses. Such conflicts appointed some
actions required to implement sustainable uses. The study was oriented to attend the
local population demand and to protect the ecosystem as a whole. The qualitative data
extracted from the interviews and the compliance with the Brazilian regulation were used
as inputs to build up the recommendation.
Keywords: FAHP, Multi-Purpose Water Resource, Sustainability Assessment.
104
TH2G (invited)
An Efficient Box Algorithm for Discrete Tricriteria
Optimization Problems
Dächert, Kerstin (*)
Klamroth, Kathrin
General multi-objective optimization problems are often solved by a sequence of parametric single objective problems, so-called scalarizations. If the set of nondominated points is
finite, and if an appropriate scalarization is employed, the entire nondominated set can be
generated. Ideally, the total number of scalarized subproblems depends polynomially on
the number of nondominated points. In the bicriteria case approaches are known which,
given an appropriate initial search space, require the solution of at most 2|N |−1 subproblems, where N denotes the nondominated set of the underlying problem. Thereby, |N |
subproblems are solved to generate all points in N , and the additional |N |−1 subproblems
are needed to ensure that no further nondominated points exist between the already generated ones (Ralphs et al., 2006). Up to now, no polynomial bounds are known for higher
dimensional problems. The best known approach has a theoretical bound of (|N | + 1)m−1
subproblems for problems with m objectives (Laumanns et al., 2006). We present a new
procedure for finding the entire nondominated set of tricriteria optimization problems for
which the number of scalarized subproblems to be solved is bounded by 3|N | − 2. This
is achieved by the definition of a new split criterion which allows to exclude redundant
parts of the search space. It can then be shown that the number of boxes, into which the
search space is decomposed, depends linearly on the number of nondominated points.
Keywords: Multi-Objective Optimization.
105
MO2C (contributed)
An Enumerative Cutting Plane Approach to Integer
Linear Vector Optimization Problems
Habenicht, Walter (*)
University of Hohenheim
The approach presented in this paper is based on the concept of intersection cuts, which
is well known in classical integer linear programming. The method starts by generating
an optimal basic solution of the lp- relaxation for some reasonable weighted sum of the
objectives. The optimal basic solution is presented to the decision maker and he is asked
to define a region around the optimal solution in objective space. In fact, we assume this
region will be a hypercube. The feasible integer points in the interior of this hypercube
are enumerated. The efficient ones are presented to the decision maker, and he is asked
to choose the best one out of them. Then, the hypercube serves as a cut-generating set.
The generated cut cuts off only those feasible integer points that have been enumerated
before. Comparing the best feasible integer solution found so far with the (non-integer)
vertices of the facet generated by the cut allows us to decide whether to stop the process
or not. If the best feasible solution it preferred to the best vertex of the facet we can
assume that under rather customary conditions the best solution is found. Otherwise, the
best vertex of the facet is used as the next basic solution of the lp-relaxation and a next
iteration is performed.
Keywords: Integer Programming, Interactive Methods, Mixed Integer Programming,
Multi-Criteria Decision Making, Multi-Objective Optimization.
106
TU2C (contributed)
An Evaluation of the Situation Judgment Test as a
Multidimensional Measure of Decision Making
Reinerman-Jones, Lauren (*)
University of Central Florida
Parchment, Avonie
Teo, Grace
Decision making is a skill often sought to quantify. This goal is achieved primarily through
the use of tasks or questionnaires. The challenge with either of these approaches is that
the metrics are often unidimensional. A widely-used multidimensional measure is the Situation Judgment Test (SJT) which comprise work scenarios, each with various possible
response options. These scenarios entail complex, real-world problems and respondents
must decide on the best choice for each. The scenarios and response options are modifiable by testers, and the problem with this is that the scenarios may not be content
valid. Although the criterion validity may still indicate the usefulness of the SJT, performance data needed to determine validity may not be available till much later. Also,
such post-hoc analysis is biased as it requires the tool to be used before its evaluation,
and personnel may have self-selected out or be released of duties before the validation
completes. This may result in the wrong test being used, which is very expensive and
time consuming. The aim of the present study is to explore a methodology for evaluating
the content and construct validity of SJT scenarios that could later be used to assist in
the selection of good decision makers. This was accomplished by administering six SJT
scenarios to 90 participants, along with the Adult Decision Making Competence (ADMC;
Bruine de Bruin, Parker & Fischhoff, 2007), Cognitive Reflection Task (CRT; Frederick,
2005), Decision Outcome Inventory (DOI; Bruine de Bruin, Parker & Fischhoff, 2007)
and moral judgment items (Greene, Morelli, Lowenberg, Nystrom & Cohen, 2008; Starcke, Polzer, Wolf & Brand, 2011). Performance on those six scenarios correlated with
two of the scales in the ADMC, the CRT, and the moral judgment items. The results
indicated that these measures can help shed light on the content validity of the SJT. Future research should examine other measures for evaluating SJTs prior to implementation.
Keywords: Decision Making, Measurement, Multi-Dimensional, Psychology, Situational
Judgment.
107
WE2G (invited)
An Evaluation of Website Upgrade Options. A Case
Study Comparison of ANFIS and RIMER
Xu, Ling (*)
The University of Manchester
Chen, Yu-Wang
Sabin, Andrada
The University of University
In this paper, two methods, Adaptive Neuro Fuzzy Inference System (ANFIS) and Belief
Rule-Base Inference Methodology Using the Evidential Reasoning Approach (RIMER),
are investigated in a real case scenario of supporting a strategic decision process: Sapico
Chemical International’s website upgrade. Literature is first analysed to structure the
problem and a bilateral approach is employed taking into account both company and
its customer opinions. The established framework is then further refined following the
opinions and requirements of Sapico. The refined framework is then populated with the
data obtained from Sapico’s manager, experts and the aggregated results of surveys conducted by Sapico. Two comparative investigations are carried out. In one investigation,
options for upgrading the company web site are evaluated through two criteria: customer
and company point of views. In the other, the options are evaluated using the same two
criteria except that customer point of view is further decomposed into sub-criteria. We
name the resulted models 1-level and 2-level models respectively. In both investigations,
instead of aggregating criteria, we use fuzzy rules for ANFIS and belief rules for RIMER
and their respective inference methods for the evaluation. The overall results show that
1-level ANFIS and RIMER models have very similar performances. The 2-level ANFIS
model performs slightly worse than the other three. When forecasting aggregated customer opinions, the 2-level RIMER model performs considerably better than the 2-level
ANFIS. With regard to explanatory power, RIMER models perform better than ANFIS
ones. RIMER is able to explain the inherent risks and advantages within every output
score. The same high explanatory power can also be noticed in RIMER when looking at
the trained rule bases. We have explored other problem contexts that the solution can be
applied. In addition to this, we have also provided Sapico with a ranking of the options
for an upgrade.
Keywords: Aggregation Schemes, Artificial Intelligence, Belief Rule-Based methodology, Fuzzy Reasoning, Neural Networks, Structuring Decisions.
108
FR2F (invited)
An Evolutionary Multiobjective Optimization
Algorithm for Diverse Solutions
Chmielewski, Hana (*)
North Carolina State University
Piper, Brian E.B.
Ranjithan, S. Ranji
Issues with multiple objectives and incompletely defined problems are frequently encountered when solving engineering problems. Evolutionary multiobjective optimization
(EMO) algorithms provide a possible method for addressing these difficulties. In this
paper, EMODS (Evolutionary Multiobjective Optimization Algorithm for Diverse Solutions) is proposed for the purposes of approximating a Pareto front and locating nearlynondominated alternative solutions with diverse solution characteristics. A hypervolumebased fitness calculation method is proposed that assigns fitness to all solutions in a
population. This fitness method allows solutions with similar levels of performance to
compete for selection based on decision space diversity. An archive is described to retain nondominated solutions and nearly-nondominated alternative solutions. Measures of
decision space diversity are defined to capture the degree to which solutions are spread
through the decision space and the distinctness of the alternative solutions that are located. The algorithm is evaluated on a range of test problems and compared against
other diversity enhancing EMO algorithms in the literature. The results show that the
proposed algorithm is competitive or superior to the compared EMO algorithms in terms
of discovering a high-quality nondominated set. Additionally, results of the decision space
diversity metrics show that the proposed algorithm achieves greater solution diversity than
other methods and discovers alternative solutions that are distinctly different from other
solutions in the decision space.
Keywords: Evolutionary Computations, Generating Alternatives, Genetic Algorithms,
Multi-Objective Optimization, Solution Diversity.
109
WE2C (contributed)
An Extension of the Fuzzy DEMATEL Method for
Group Decision Making
Almulhim, Tarifa (*)
Mikhailov, Ludmil
Xu, Ling
The Fuzzy Decision Making Trial and Evaluation Laboratory (Fuzzy DEMATEL) method
is one of the Multi Criteria Decision Making tools, which can structure complex problems
through visual structural model and analyze the causal relationship between factors. In
the existing literature, the Fuzzy DEMATEL method assumes that all the decision makers
are equal in their relative weights of importance in the problem considered. However, in
some complex group decision making problems this has its drawbacks. This paper proposes a fuzzy DEMATEL method for group decision-making with non-equal importance
weights of individual members in the group. The directed influential degrees between
pair-wise factors are expressed as fuzzy numbers in order to model the uncertainty and
imprecision judgments from the group’s members. Unlike the known Fuzzy DEMATEL,
the proposed method considers the importance weights of individual members in the group
and presents those weights as either crisp numbers or fuzzy numbers, which are used in a
fuzzy aggregation procedure. In this procedure, the Weighted Arithmetic Mean method
is applied for individual members’ weights of importance, represented as crisp values,
whereas, the Fuzzy Weighted Arithmetic Mean method is used for the fuzzy weights. For
assisting decision makers to apply the proposed method, a prototype of a decision support
tool is developed using the MATLAB Software. A comparison between the proposed and
the traditional Fuzzy DEMATEL methods is illustrated with a numerical example. The
results show that the proposed method is more suitable and reasonable than the traditional Fuzzy DEMATEL method.
Keywords: Fuzzy DEMATEL, Group Decisions Making, Importance Weights, MultiCriteria Decision Making.
110
TU3F (contributed)
An Improved MOPSO Algorithm for Multiobjective
Bilevel Linear Problems
Alves, Maria João (*)
Univeristy of Coimbra
Bilevel programs allow to model optimization problems with a hierarchical structure between two decision makers (the leader and the follower), who make decisions sequentially.
The two decision makers pursue different objectives in a non-cooperative manner by controlling different sets of variables, subject to interdependent constraints. The leader makes
his/her decisions first, thus reducing the set of feasible choices available to the follower.
The follower reacts by choosing an optimal candidate according to his/her objective function on the feasible region restricted by the leader. This choice also affects the value of
the leader’s objective function, so the leader must anticipate the reactions of the follower.
A bilevel programming problem is very difficult to solve, even the linear case. Multiple
objectives at one or both levels add further difficulties to deal with the problem. In this
work we address the bilevel linear programming problem with multiple objective functions
at the upper level and a single objective function at the lower level. We have previously
developed and tested a multiobjective particle swarm optimization (MOPSO) algorithm
to solve this type of problems, which aims to generate a good approximation of the entire
Pareto front. A new technique for the “global best” selection within the MOPSO algorithm was tested, which revealed a good convergence towards the Pareto front but not so
good in the diversity of the obtained solutions. We now propose an improved MOPSO
algorithm for the same class of problems, which uses a hybrid strategy for the “global
best” selection and an adaptive mutation operator. This operator uses information from
the candidate solutions in the “nondominated archive” and also properties of the problem
to adjust probabilities of mutation for the values of variables. The incorporation of these
mechanisms improves the previous algorithm on all tested problems. The algorithm and
computational results are presented.
Keywords: Hierarchical Optimization, Metaheuristics, Multi-Objective Optimization.
111
WE1B (contributed)
An Integrated AHP&VIKOR Method for Hospital Site
Selection
Yılmaz, Hafize (*)
Hali¸c University
G¨
ul, Sait
Halic University
Topcu, Ilker
Optimal site selection issue is one of the most important decision making problems for
the investors because of the problem’s complicated decision processes relating with the
number of criteria which should be considered attentively while making final decision.
Particularly, the site selection for a new hospital has many criteria that are required to
be considered, i.e. its investment cost, impact to the nature. In case of public investors
like general or local government and municipal authority, erroneous or inefficient decisions
may cause excessive costs on city budget or damages to the environment. In this study, we
propose an integrated multiple attribute decision making (MADM) method under fuzzy
environment. Today’s world encounters more complex problems with compared to the
past. In the same manner, the decision problems have some difficulties to be defined
precisely, because they can be affected from different and discrete factors or conflicting
criteria and have multiple objectives. In this study, we utilize fuzzy logic in order to deal
with Analytic Hierarchy Process (AHP) and Vise Kriterijumska Optimizajica I Kompromisno Resenje (VIKOR) methods under imprecise environment. Also, we handled the
hospital site selection problem as a group decision making problem and appealed more
than one decision experts’ knowledge and experiences. The usage of MADM methods for
hospital site selection is a nadir research area in literature. This study proposes a novel
approach for hospital site selection by utilizing fuzzified MADM methods in an integrated
way under imprecise environment. Briefly, AHP is used to determine the importance
weights of the selection criteria affecting the site of the hospital; and VIKOR analyses the
alternative sites and suggests the closest one(s) to the ideal solution.
Keywords: Analytic Hierarchy Process, Fuzzy Logic, Hospital Site Selection, VIKOR.
112
MO4B (invited)
An Interactive Algorithm for Bi-objective Routing
Problems
Tezcaner, Diclehan (*)
Middle East Technical University
Koksalan, Murat
In this study, we develop a branch and bound based algorithm for bi-criteria integer programs that finds the most preferred solution of a decision maker (DM) with a quasiconvex
preference function to be minimized. We demonstrate our algorithm on the bi-objective
routing problem for unmanned air vehicles (UAV) which is assumed to travel on a terrain
discretely approximated by grids. The algorithm finds the best route of a DM between a
number of targets in the presence of two objectives. Finding the efficient paths between
targets can be modeled as a multi-objective shortest path problem. Finding the efficient
tours made up of the efficient paths can be modeled as a multi-objective traveling salesperson problem. Therefore, multi-objective routing problem is a combination of these two
combinatorial problems. In our interactive algorithm, we need all the efficient shortest
paths between all pairs of targets as an input to the branch and bound procedure we
develop. We establish some rules that decrease the number of efficient paths required for
the solution of the branch and bound algorithm. We also address the more general case
where the terrain is defined in a continuous space for the UAV routing problem. We study
the problem of identifying efficient paths for this case.
Keywords: Bi-Objective Routing, Combinatorial Optimization, Interactive Methods.
113
MO2C (contributed)
An Interactive Algorithm for Multi-objective Integer
Programs
Koksalan, Murat (*)
Korhonen, Pekka
Lokman, Banu Wallenius, Jyrki
Aalto University
We develop an interactive algorithm to find the most preferred solution for multi-objective
integer programs. We assume that the decision maker’s (DM’s) preferences are consistent
with a nondecreasing quasiconcave value function. Based on the properties of the value
function and pairwise preference information obtained from the DM, we generate constraints to eliminate inferior regions with respect to cones. We also develop a variation
where we eliminate regions that are close to being dominated by the cones in addition
to the regions dominated by the cones. The algorithm continues iteratively and approximates the most preferred solution for any multi-objective integer program with a desired
level of accuracy provided that the DM’s preferences are consistent with a nondecreasing
quasiconcave value function. The algorithm allows the DM to change the desired level of
accuracy throughout the solution process. We test the performance of the algorithm on
a set of multi-objective combinatorial optimization problems. It performs well in terms
of the quality of the solution found, the solution time, and the required preference information from the DM.
Keywords: Integer Programming, Interactive Methods, Multi-Objective Optimization.
114
MO2C (contributed)
An Interactive Algorithm for Multiobjective Mixed
Integer Programming Problems
Ozpeynirci, Ozgur (*)
Izmir University
Koksalan, Murat
Lokman, Banu
Aalto University
In this study, we aim to find the most preferred nondominated point of a multiobjective
mixed integer programming problem with p objective functions. Without loss of generality, we assume all objectives are to be maximized. We first consider an underlying linear
value function. It is known that an extreme supported nondominated (ESN) point that is
preferred to its adjacent ESN points is the most preferred point for an underlying linear
value function. We first develop an exact procedure for finding all adjacent ESN points
of a given ESN point. Utilizing this procedure, we develop an interactive algorithm that
operates in the objective space and starts from an arbitrary extreme supported nondominated (ESN) point. It evaluates adjacent ESN points using past and current preference
information gathered through the decision maker interactions. It searches for preferred
regions utilizing all the available information and terminates when it finds the ESN point
preferred to all its adjacent ESN points. We also generalize the algorithm for the quasiconcave value function case. In this case, we again identify an ESN point that is preferred
to all its adjacent ESN points. We then search for preferred unsupported nondominated
points within the neighborhood of the identified ESN point. Interacting with the decision
maker, the algorithm converges to the most preferred point. We conduct computational
tests on multiobjective assignment and knapsack problems.
Keywords: Adjacency, Interactive Methods, Value Function.
115
FR3C (contributed)
An Interactive Approach to the Bi-objective Inventory
Routing Problem
Huber, Sandra (*)
Helmut-Schmidt-University
Geiger, Martin Josef
Helmut-Schmidt-Universtiy Hamburg
Sevaux, Marc
Université de Bretagne Sud
The talk presents an interactive approach for the bi-objective inventory routing problem (IRP). The IRP combines delivery quantity decisions with a vehicle routing problem
into a simultaneous model. In this problem setting, a considerable tradeoff between the
minimization of the inventory levels on the one hand, and the minimization of the transportation effort (i.e. the traveled distances) on the other hand is present. A local search
approach on the basis of a multi-point hillclimber is used for computing solutions. After
each solution construction phase, the decision maker is actively involved in the intermediate decision making process. He/she is allowed to state personal preferences, thus guiding
the subsequently derived search direction. We investigate the effectiveness of a reference
point-based approach for the problem at hand. A prototypical implementation of the
concept into a running system has been made available, and experiments can and have
been conducted on its basis. In addition to the study of the above mentioned logistical
problem, we also look into ideas of how to simulate a decision maker in Silico. This related to our motivation of obtaining more general insights into the behavior of interactive
search-and-decision-making systems for such multi-objective combinatorial optimization
problems.
Keywords: Inventory Routing Problem, Logistics, Multi-Objective Optimization, Transportation.
116
TU3G (contributed)
An Interactive Tool for Multi-Objective Linear
Programming
Henggeler Antunes, Carlos (*)
University of Coimbra
Alves, Maria João
Univeristy of Coimbra
In addition to exposing students to concepts and methods, teaching optimization algorithms in engineering, economics and management courses requires a hands-on approach
using tools that enable them experimenting and exercising critical thinking. This is quite
important in teaching multi-objective optimization since the non-dominated solution set
needs to be identified, desirably in a constructive manner that may provide further information about the trade-offs involved. In practice, presenting a decision maker (DM)
with a large set of non-dominated solutions does not convey usable information for actual
decision support. The involvement of the DM by providing indications about his/her
preferences is essential to guide and reduce the scope of the search, thus minimizing
both the computational effort and guaranteeing that the new solutions computed are
more in agreement with his/her (evolving) preferences. By intertwining computation and
judgment phases, interactive methods allow for a progressive shaping of the DM’s preferences as the selective characterization of non-dominated solutions develops. Therefore,
interactive computer tools are indispensable to enhance the students’ technical skills and
provide training as DMs, integrating different interactive methods to deal with MOLP
using different solution computation techniques, search strategies, preference elicitation
requirements, visual interaction mechanisms and result displays. This is being used as
the entrance door for improving students’ understanding of the main issues at stake in
MOP, before progressing to more technically demanding topics, in which the main theoretical concepts can be perceived through experimentation at their own pace. Moreover,
it allows students playing the role of actual DMs and exploit different search strategies
and trade-offs leading to distinct non-dominated solutions. A computer tool is presented
integrating distinct interactive approaches in a consistent operational framework.
Keywords: Decision Support Systems, Education, Interactive Methods, Multi-Objective
Optimization.
117
TH2D (invited)
An Optimization Model for Group Decision Making:
Weighting and Ranking with Incomplete Fuzzy
Preference Relations
Ruiz-Tagle, Mauricio (*)
Universidad Austral de Chile
Dopazo, Esther
Universidad Politécnica de Madrid
We consider a finite set of alternatives that have to be ranked according fuzzy preference
relations given by multiple experts. In real-world problems it is usual that experts are
not able to efficiently provide their preferences on some aspects of the problem because
large number of alternatives, limited expertise related to some problem domain, not available data, etc. In this paper a model is presented to generate a group priority vector of
the alternatives that reflects as best as possible the incomplete fuzzy preference relations
given by the experts. The resulting optimization problems are solved by using goal programming techniques. Furthermore we discuss sufficient conditions on the structure of
the data available that guarantee the existence of a priority vector.
Keywords: Fuzzy Sets, Goal Programming, Group Decisions Making, Meta-Goal, Preference Aggregation.
118
WE1D (invited)
Analytical Approximation of the Influence of Capacity
on the Economic and Ecological Performance of
Bioenergy Plants
Lauven, Lars-Peter (*)
University of Göttingen
In this work, economically optimal capacities for bioenergy plants are determined by
applying a nonlinear optimization approach. Both economies of scale and required transportation distances are approximated with appropriate functions to identify optimal capacities with regard to the required investment, input and output prices and the available
amount of biomass in the proximity of the envisioned plant. While larger bioenergy plant
capacities lead to improved specific production costs, they also increase biomass transportation distances. In addition to identifying economically optimal production capacities
for a number of scenarios, the investigation therefore also aims to quantify the effect on
transportation-related GHG emissions and discusses side-effects of concentrated energy
plant production.
Keywords: Bioenergy, Epsilon-Constraint Method, Greenhousegas Emissions, Non-Linear Programming, Plant Capacity Planning.
119
TH4B (contributed)
Analyzing the Provision of Agricultural Public Goods:
The Case of Irrigated Olive Groves in Southern Spain
Villanueva-Rodr´ıguez, Anastasio (*)
Inst. Agric. and Fisheries Research
Arriaza, Manuel
Institute of Agricultural and Fisheries Research
Gómez-Limón, José A.
University of Cordoba
Nekhay, Olexandr
The analysis of the joint production of private and public goods from farming activities is
a fertile research field in agricultural and environmental economics aiming at supporting
public decision-making regarding a better governance of the agricultural sector. These
joint production processes are typically characterized by a high level of complexity derived
from the intense relationship between the productions of both kinds of output. Hence, an
integrated approach is strongly recommended to study the provision of agricultural public
goods and the design of public intervention in this sector. Among the methodologies that
apply such approach in this research field, the Analytic Network Process (ANP) is one
of the most promising because it allows to consider interdependencies among different
elements of the system studied. Here, we use the ANP to assess how the public goods
provided by farming activities can be affected by farmers’ decision-making, and thus how
effective policy intervention could be. This would be helpful for policy-makers to identify
intervention priorities, enabling policy instruments to be primarily focused on the more
modifiable public goods provisions. This methodological approach is empirically applied
to a particular farming system; the irrigated olive groves of southern Spain. Through the
ANP network designed for this case study, we have identified the public goods more influenced by farmers’ decisions, namely soil fertility, farmland biodiversity and visual quality
of agricultural landscapes. In addition, the most influential factors affecting public goods
provision have been identified. Particularly, agricultural practices dealing with fertilization, soil and irrigation management, as well as structural variables regarding farm size,
density of planting and variety of olive tree are worth acknowledging. These results are
useful for supporting agricultural policy decision-making to enhance an adequate management of this farming system.
Keywords: Agricultural Public Goods, Analytic Network Process, Irrigated Olive Grove ,
Southern Spain.
120
MO4E (contributed)
Application of an Intelligent Decision System to
Assess Students’ Academic Performance in University
Early Admission
Chen, Yu-Wang (*)
Chen, Yue
Shanghai Jiao Tong University
Xu, Ling
Yang, Jian-Bo
This paper presents how the evidential reasoning approach of multi-criteria decision analysis, with the support of the Intelligent Decision System (IDS), can be used to support
the data analysis and decision making process in university early admission. Following a
literature review on the academic performance assessment problem and relevant statistical
and artificial intelligence techniques, the paper introduces the criteria which are used to
assess students’ academic performance in practice, and then describes the process of using IDS for model implementation, information collection and aggregation, and graphical
presentation of outcomes. The academic performance assessment with the application of
IDS can generate informative results to support the decision making process in practical
university early admission.
Keywords: Academic Performance Assessment, Early Admission, Evidential Reasoning, Intelligent Decision System.
121
TH3E (contributed)
Applying of Fuzzy Logic to Decision-Making Control
of the Ship Motion
Malecki, Jozef (*)
Polish Naval Academy
Fuzzy logic systems find wide practical applications to making decision ranging from soft
control in consumer products to accurate control and modeling of complex nonlinear systems of ship. A paper showed a possibility of using a fuzzy logic environment as a method
making-decision to solve the problem related to determining an optimal trajectory in ship
motion. It is possible to effectively solve tasks of determining a trajectory movement
of a ship as a multistage decision-making process in a fuzzy environment. A paper explores fuzzy logic and how it helps navigators to solve control problems commonly found
in autopilot applications – automatic control systems of the ship. Fuzzy logic, which
mathematically emulates human reasoning, provides an intuitive way to design function
blocks for intelligent control systems of the ship motion. Automatic control systems deploying fuzzy logic can improve the management of uncertain variables, such as making
decision to control of the ship. Basic modules of the automatic control system consists
of a Fuzzy Autopilot, a Thrust Distribution Module, a Propulsion System, the Ship and
Filtering-Estimation Module. The Autopilot computes command signals τ d comparing
desired ship’s position xd, orientation and velocities with their current estimates. Corresponding values of propellers thrust f are calculated in the Thrust Distribution Module
and transmitted as control input to the Propulsion System. The propulsion system generates a control signal consists of thrust forces actuators of the Ship. On the ship are
acting disturbances d. The control autopilot adjustments are modified by a position and
velocity data from the Filtering-Estimation Module. The paper consists of the following
sections. It starts with a brief description of dynamical and kinematical equations of the
ship’s motion. Then a fuzzy control law and a power distribution algorithm are presented.
Next some computer simulation results and conclusions are provided.
Keywords: Applications of MCDM, Control, Decision Making, Dynamic Systems, Fuzzy
Logic, Marine Engineering Systems.
122
FR3E (contributed)
Applying the Utadis Method to the Identification of
Key Factors of Organisational Commitment: A Case
Study in Petrobras
Autran M. Gomes, Luiz F. (*)
Ibmec/RJ
Gon¸calves, Marcos E.L.
Petrobras
Rangel, Luis A.D.
Fluminense Federal University
The key objective of this study was to perform a classification of the items of an organisational ambience survey at Petrobras in relation to its impact on organizational
commitment. The method used for sorting the items was UTADIS, using criteria defined
from organisational commitment measurement questionnaires. The items with highest
impact on organizational commitment were then sorted. The classification proposed was
used as a basis for a discussion between employees and the HR team. That discussion
led to defining the priorities for the ambience improvement action plan. The secondary
objective of the study was creating an evaluation and prioritisation process which could
be used in other management areas of the company. The overall results showed the applicability of the method to the problem, opening the possibility of it being used in other
management areas of Petrobras.
Keywords: Human Resources, Linear Programming, Multi-Criteria Sorting, UTADIS.
123
MO4D (invited)
Approximations for the Multi-objective Spanning Tree
Problem
Ismaili, Anisse (*)
LIP6 - UPMC
The multi-objective spanning tree problem (MOST) is a very challenging problem due
to the combinatorial blow-up of the Pareto set as the size of the problem increases. In
the bi-objective case, exact non-polynomial two-phase methods hold a good time cost
see e.g [Spanjaard et al. 2008]. These methods, first compute the set of Pareto optimal
solutions which are supported by a linear combination of the multi-objective cost vectors,
then apply branch and bound to compute the non-supported Pareto optimal solutions.
Nevertheless, generalizing such 2-phases methods to problems involving p > 2 criteria
is far from trivial and alternative solutions methods are worth investigating. Moreover,
it is often useless to generate the whole Pareto set (that grows exponentially with the
problem) and a more reasonable objective is to only find a small epsilon-approximated
covering of the Pareto-set [Papadimitriou et al. 2000]. To the best of our knowledge, for
p > 2, the best known pseudo polynomial algorithm addressing the MOST [Papadimitriou
et al. 2000] relies on the matrix-tree theorem [Kirchhoff]. It computes the determinant of
a Laplacian matrix over polynomials by using a dynamic programming algorithm without
divisions [Mahajan et al. 1999, Rote 2001]. By rounding the costs, the resulting theoretical FPTAS is very expensive, despite polynomial time. After a simplification of this
FPTAS, we experiment different dynamic programming schemes to compute either the
exact Pareto-set or an epsilon-covering. Their optimal sub-structures are defined by the
set of vertices which are covered and connected. The first scheme iteratively adds vertices
one by one to the sub-structures. The second scheme merges the sub-structures. Both
algorithms can be modified to build an epsilon-approximated covering of the Pareto-set.
Ultimately, we experiment the contribution of multi-objective heuristic search to state
spaces with similar definitions.
Keywords: Approximation, Combinatorial Optimization, Multi-Objective Optimization,
Spanning Tree.
124
MO4C (invited)
Approximations of Set-Valued Optimization Problems
López Montoya, Rubén (*)
Universidad Católica de la Ssma Concepción
Hernández, Elvira
Universidad Nacional de Educación a Distancia (UNED)
The aim of this work is to introduce a notion of convergence for set-valued mappings
and to show that this notion is suitable for studying set-valued optimization problems via
approximation procedures.
Keywords: Efficiency, Mathematical programming, Pareto Optimal Solutions, Sensitivity Analysis.
125
TU4G (invited)
Assessing the Efficiency of Equity Mutual Funds. A
Fuzzy DEA Approach
Baeza-Sampere, Ismael (*) Coll-Serrano, Vicente
M’Zali, Bouchra
UQAM
Although several DEA models have been proposed in the literature for mutual funds performance evaluation, few of them incorporate non-financial criteria. In this paper a fuzzy
DEA model is used, allowing mutual funds relative performance evaluation in a more
realistic and flexible way. We examine efficiency of 40 US large cap equity mutual funds
based not only on financial variables but also on non-financial ones. To achieve this aim,
we extend Basso and Funari’s mutual funds’ ethical level proposing a more reliable fuzzy
measure of the Social, Environmental Responsibility degree of equity mutual funds. It
relies on the Corporate Social Performance of the companies invested in by the mutual
funds and on the Quality of the Management in terms of the transparency and credibility
degree of the non-financial information provided by the mutual funds. We can conclude
that Socially Responsible Mutual Funds show a better behaviour in terms of efficiency
than conventional funds.
Keywords: Efficiency, Equity Mutual Funds, Fuzzy DEA, Socially Responsible Investment.
126
TU5B (contributed)
Assessment of Renewable Energies based on Fuzzy and
Qualitative Multi-Criteria Decision Making: a
Comparison of two Wethodologies
Afsordegan, Arayeh (*)
Cremades, Lázaro V. Sanchez, Monica
Zahedi, Siamak
Agell, N´
uria
ESADE, Universitat Ramon Llull
Renewable energies have become a crucial issue in energy sector research due to their
increasingly positive impact on sustainability. Current studies focus on the impact of
existing sources of renewable energies, as well as their comparison and possible combinations. The assessment and selection of the most suitable types of energy in a geographical
area is a complex problem, involving technical, economic, environmental, political, and
social criteria. To establish a methodology to select the best option depending on different criteria in each scenario, this paper compares two multi-criteria decision making
(MCDM) approaches based on linguistic label assessment for renewable energies. The
first approach consists of a modified fuzzy Topsis methodology introduced by Kaya and
Kahraman in 2011. The second approach, introduced by Agell et al. in 2012, is based on
qualitative reasoning techniques for ranking multi-attribute alternatives in group decisionmaking with linguistic labels. This second methodology has achieved interesting results
in various managerial applications, but has not yet been applied in the energy sector.
An example, based on data provided in a paper by Kaya and Kahraman in 2011, is used
to illustrate the mechanisms employed in both approaches and analyse their similarities
and differences. We consider seven energy alternatives, six renewable (solar, wind, nuclear, biomass, hydraulic, and combined heat and power) and one conventional. Both
MCDM methods are performed on the basis of nine criteria: efficiency, energy efficiency,
investment cost, operation and maintenance cost, NOX emission, CO2 emission, land
use, social acceptability, and job creation – as conveniently weighted by a group of three
experts using the analytic hierarchy process (AHP). The obtained results are presented
and analysed using both methods in five scenarios.
Keywords: Analytic Hierarchy Process, Energy System, Fuzzy Sets, Group Decisions
Making, Multi-Criteria Decision Making, Qualitative Reasoning.
127
WE1B (contributed)
Assessment of the Relationship between Planned
Obsolescence and Product Eco-Design based on
MCDA
Casas Páez, Pedro Nicolás (*)
Universidad Nacional de Colombia
Cortes Aldana, Felix Antonio
Universidad Nacional de Colombia
Purpose: To evaluate the linkage between eco-design and desired obsolescence in electronic manufacturers and systems integrators. Design/methodology/approach: The Analytic Hierarchical Process is a Multi-Criteria Decision Analysis methodology, used to
formalize the intuitive understanding of complex problems by the construction of a Hierarchical Structure. The decision model is presented, taking into account the stakeholders
of a local electronic system integrator. Findings: The study develops a framework for
measuring the relative importance of obsolescence models based on the application of
eco-design concepts. Due the particular context and considerations presented on the
study, the model may need to be customized to fit the organization’s structure. Research limitations/implications: The model presented is dependent on the concept of the
decision-maker team, and the generalizability of findings on this model to other products or different environmental situations may be limited. Adding further characteristics
related to the total product may change the presented perceptual weightings. Practical implications: This paper addresses one of the challenges of the green marketing and
the product administration, in determining which obsolescence model is preferred when
taking into account the environmental eco-design principles of the base product. Originality/value: This paper offers practical help by providing a decision model that assists in
determining which obsolescence model is most preferred based on firm’s judgment around
eco-design attributes and system design, as well as the prioritization of those attributes.
Keywords: Analytic Hierarchy Process, Decision Making, Multi-Criteria Decision Analysis, Obsolescence, Product Development.
128
MO2E (contributed)
Augmented Goal Programming with Multi Fuzzy
Targets and Coefficients
Mouslim, Hocine (*)
Tlemcen University
Belmokaddem, Mustapha
Tlemcen University
Melloul, Sakina
Tlemcen University
Decision making is becoming more and more complex for managers, mainly in the multicriteria problems. Multi-choice goal programming (MCGP), and Multi-segment goal programming (MSGP) are considered as powerful tools to solve this type of problems. In
these models, the decision maker considers the multi targets and the coefficients (parameters) of each goal as precise and well known. However, in some of the decision making
problems, there may exist situations where the manager could not be interested in presenting his prefered targets and coefficients in a precise manner. To deal with such situations,
Tabrizi et al. (2012) have lately reformulated (MCGP) in order to model the targets
fuzziness. In their model, the only functions which are used are the triangular membership ones which do not reflect adequately the decision maker’s (DM’s) preferences that
are considered an essential element for modelling the parameters (targets and coefficients)
fuzziness. In this paper, new ideas are presented to reformulate (MCGP), and (MSGP)
in a single formulation in which the decision maker’s (DM’s) preferences are taken into
consideration for modelling the targets and also coefficients for all the types of goals in
an imprecise environment. Moreover, the concept of indifference and veto thresholds is
used in the new formulation for characterizing the imprecision and the preferences of the
goals. This model is called Augmented Goal Programming with multi fuzzy targets and
coefficients (AGP). A numerical example is given to demonstrate the validity and strength
of the new model.
Keywords: Augmented Goal Programming, Decision Maker’s Preferences, Decision Making, Indifference Thresholds, Multi-Choice Goal Programming, Multi-Parameter Functions.
129
TU4F (contributed)
Automating Mechanism Design using Evolutionary
Multi-Criteria Optimisation
Chandra, Arjun (*)
University of Oslo
Allmendinger, Richard
University College London
Lewis, Peter
University of Birmingham
Market-based interaction mechanisms realise the allocation of contended resources across
multiple strategic agents operating in multi-agent systems or electronic commerce environments. Designing these mechanisms has traditionally been carried out by hand, and
more recently by automation. Measuring the performance of these mechanisms requires
them to be evaluated with respect to multiple conflicting system-wide criteria, which can
often be non-linear, noisy, and expensive to compute. Automated mechanism design involves discovering mechanisms that perform well on such criteria, subject to constraints
that limit the search space to a class of mechanisms of interest. We focus on mechanisms
that induce the allocation of m identical resource units across n agents (m < n) in a
unit-demand setting. Considered is the Vickrey-Clarke-Groves redistribution mechanism
class, which is further constrained to being strategy-proof, non-deficit, and individually
rational. The system-wide performance criteria are defined as the average case welfare,
envy, and disproportionality rates exhibited by the mechanism, with uncertainty in the
type profile of agents imposing noisy and expensive evaluations. Whilst previous research has looked at automated design using a single aggregated criterion, we propose the
application of evolutionary multi-criteria optimisation, and exemplify this approach by
providing approximations to Pareto-optimal redistribution mechanisms for the resource
allocation scenario at hand. We show that the approach gives not only insights into the
range of different redistribution mechanisms, but also reveals trade-off mechanisms with
superior envy and disproportionality rates when compared with state-of-the-art redistribution mechanism designed both by hand and by previous automated mechanism design
approaches.
Keywords: Automated Mechanism Design, E-Commerce, Economics, Expensive Evaluations, Multi-Agent Systems, Multi-Objective Optimization, Redistribution Mechanisms.
130
TU4C (contributed)
Best Practices for Spatial Multicriteria Evaluation: a
Critical Review
Ferretti, Valentina (*)
Montibeller, Gilberto
London School of Economics
Environmental decision-making often involve multiple impacts and conflicting objectives.
Decisions in this context are always complex due to several features: (i) their public nature requires transparency and justification, (ii) the presence of multiple stakeholders with
different values, (iii) the need for legitimacy entails participation in the decision-making
process, (iv) the existence of a spatial distribution of the different impacts increases the
technical complexity of the evaluation, and, finally, (v) there are uncertainties concerning
the spatial distribution of impacts and policy makers’ preferences, which must be considered in the analysis. These challenges may explain a growing interest on the integration of
Multiple Criteria Decision Aiding (MCDA) with Geographic Information Systems (GIS),
as it provides a more efficient way of supporting such decision problems. It allows both the
generation and the comparison of spatial alternatives, taking into account policy makers’
conflicting objectives and their preferences as well as spatial information. The scientific
literature contains many contributions on MCDA-GIS developments and applications in
a variety of domains and several MCDA methods are currently used in order to aggregate
the different dimensions of the analysis. In this paper we are considering best practices for
spatial multicriteria evaluation and exploring to which extent different popular methods
employed in practice comply with these best-practice standards. The review is organized
along the key decision analytic phases (model structuring, preference elicitation, aggregation of partial performances, sensitivity analysis and overall communication).We attempt
to provide a critical and balanced analysis of the use of the different MCDA methods in
the environmental decision-making context, and suggest some opportunities for further
developments in this field.
Keywords: Decision Analysis, Environmental Decision Making, Multi-Attribute Utility
Theory, Multi-Criteria Decision Making, Preference Aggregation, Problem Structuring,
Spatial Multi-Criteria Evaluation, Value Focus Thinking.
131
TH3E (invited)
Bi-Criteria Optimisation using Surrogate Modelling
for Dynamic Process Design
Fraga, Eric (*)
Beck, Joakim
The goal in multi-criteria optimisation is to identify a set of Pareto optimal points that
are well spread out in the objective space, with as few evaluations of the computer model
as possible. In the context of computationally expensive models, replacing the model with
a surrogate in the optimisation cycle may be necessary to solve these problems. Starting with a low-fidelity surrogate model, the surrogate is updated after each iteration of
the optimisation. This sequential update procedure generally requires fewer evaluations
of the full model than starting with a high fidelity surrogate. Efficient global optimisation (EGO) with Gaussian Process (GP) surrogate models has been widely used for
single-objective optimisation. EGO uses the “expected improvement” criterion to select
a promising point to update the surrogate model. Recently, Emmerich et al. (2011) used
the concept of hypervolume to formulate a generalisation of the expected improvement
criterion to multi-criteria optimisation. For single objective optimisation, another criterion is the “expected information gain”, a measure of the conditional mutual information
between the points that have been selected and the ones that have not. A good approximation of the expected information gain is available in GP surrogate models, where the
prior knowledge is in the points selected so far. Curiosity driven optimisation involves
a trade-off between the expected improvement and the expected information gain. This
paper investigates different ways of defining information gain in the context of bi-criteria
optimisation and explores their impact on the spacing, richness, spread, and coverage of
the Pareto front. The use of the expected information gain to update the GP surrogate
model for bi-criteria optimisation is demonstrated for the design of pressure swing adsorption processes, maximising the conflicting criteria of purity and recovery of the product.
Keywords: Multi-Objective Optimization, Statistics, Surrogate Modeling.
132
TH4G (contributed)
Bi-objective Traffic Assignment with Multiple User
Classes: A Time Surplus Approach
Perederieieva, Olga (*)
Ehrgott, Matthias
Raith, Andrea
Wang, Judith Y. T.
University of Leeds
Traffic congestion is an issue in most cities worldwide. One way to model and analyse
the effect of congestion on route choice behaviour is traffic assignment (TA). Conventional TA models the behaviour of travellers by assuming that all drivers are selfish and
tend to choose the fastest routes from their origin to their destination. As a result an
equilibrium state is achieved, when no one has an incentive to switch to another route.
Therefore, it is assumed that all drivers make their travel decisions regarding travel time
only. However, this is not true in general. According to empirical studies other important
factors are travel time reliability and monetary cost. In the literature on the TA problem
where two or more route choice objectives are explicitly distinguished the majority of
the models form a weighted sum of the objectives. This approach allows to find only a
subset of all equilibrium solutions. We propose to use a conceptually different approach
- the bi-objective user equilibrium. It considers two objectives separately (travel time
and toll) and allows multiple solutions. In order to model user preferences, we apply the
time-surplus maximisation model (TSMaxBUE) which can identify one of the solutions.
Time surplus is defined as the maximum time a user is willing to spend minus the actual
time spent. The maximum time a user is willing to spend is modelled as an indifference
curve - a non-linear function that depends on the path toll. All drivers are divided into
classes each with its own indifference curve that models preferences in this group. We
show that this model admits a mathematical programming formulation and we adapt
some conventional TA algorithms to solve TSMaxBUE with multiple user classes. The
TSMaxBUE model allows to obtain various traffic patterns by changing the indifference
curves. We observe that this framework is general enough to cover any situation with a
flow dependent and a flow independent component of path cost.
Keywords: Bi-Objective Traffic Assignment, Time Surplus, User Equilibrium.
133
MO3E (contributed)
Biases and Path Dependency in the Even Swaps
Method
Hämäläinen, Raimo (*)
Aalto University
Lahtinen, Tuomas
Aalto University
Even Swaps is a multicriteria decision support method introduced by Hammond et al.
(1999) in their popular book Smart Choices. In the method, a decision maker carries
out a sequence of trade-offs to identify the most preferred alternative out of a set of alternatives. Each sequence that the DM follows creates a path for the decision process.
In principle, each path should lead to the same choice. We conducted an experimental
study with the SMART-SWAPS software by Hämäläinen et al. (2003) to see if this is
the case in practice too. The results show that path dependency does exist. We explain
how the phenomenon relates to the scale compatibility bias, which causes the dimension
that is adjusted in a trade-off question, to receive extra weight (Delquié 1993). Besides
the scale compatibility bias, we suggest that also the loss aversion may play a role. We
argue that the sequential steps in the Even Swaps process can accumulate the biases on
some paths, which leads to path dependency. We suggest that it is advisable to do the
process repeatedly and check if different paths lead to the same choices. Here computer
support provided by the SMART-SWAPS software can help in practice as it allows easy
backtracking of the process. References: Delquié, P. 1993. Inconsistent Trade-offs between Attributes: New Evidence in Preference Assessment Biases. Management Science,
39(11), 1382-1395. Hammond, J. S., Keeney, R. L., Raiffa, H. 1999. Smart Choices. A
Practical Guide to Making Better Decisions. Harward Business School Press, Boston,
MA. Hämäläinen, R. P., Mustajoki, J., Alanaatu, P., Karttunen, V., Arstila, A. 2003.
Smart-Swaps – Smart Choices with Even Swaps. Computer software, Systems Analysis
Laboratory, Aalto University, http://www.smart-swaps.hut.fi.
Keywords: Bias, Decision Analysis, Decision Support, Even Swaps, Loss Aversion, Scale
Compatibility, Smart-Swaps.
134
WE2F (invited)
Bidirectional versus Unidirectional Heuristic Search for
Multiojective Optimization in State Space Graphs
Galand, Lucie (*)
LAMSADE - University Paris-Dauphine
Ismaili, Anisse Perny, Patrice
LIP6 - UPMC
Spanjaard, Olivier
University Pierre et Marie Curie
Multiobjective heuristic search in state space graphs generally aims at determining the
exact set of Pareto-optimal cost vectors associated to solution paths or an approximation
of this Pareto set. When a preference model is available under the form of scalarizing
function f, the search effort merely focuses on the direct determination of an f-optimal
tradeoff within the Pareto-set. The solution algorithms proposed in this area rely on unidirectional approaches that develop a front of Pareto-optimal labels attached to subpaths
from the initial node s to the goal nodes. When there is a single goal node t, explicitly
known, and when search operators are reversible, a bidirectional search approach is possible, interleaving and interacting searches, forward from s and backward from t. Although
the respective advantages of unidirectional approaches versus bidirectionnal approaches
have been widely discussed in the singleobjective case, they have not been investigated in
the setting of multiobjective search. The aim of this paper is to propose a first implementation of multiobjective bidirectional heuristic search and to empirically assess his value
compared to unidirectional search. Our comparison concerns both the determination of
the entire Pareto set and the determination of OWA-optimal tradeoffs within the Pareto
set. We provide results of numerical tests performed on random instances of path-planning
problems, both in terms of number of nodes expanded and in terms of computation times.
Keywords: Bidirectional Heuristic Search, Multi-Objective Optimization, Multi-Objective
Shortest Path Problem, OWA Operator.
135
MO5H (poster)
Cautious Solutions in Multi-Scenario Bargaining. An
Application to Union-Firm Negotiation.
Rubiales , Victoriana (*)
University of Seville
Mármol, Amparo Mar´ıa
Monroy, Luisa
In this paper we address a general two-person bargaining problem under uncertainty. To
this end, several states of nature or future scenarios are considered. We propose a solution
concept based on the distance to a utopian minimum outcome vector, which guarantees
conservative levels of achievement for the agents. This cautious solution is obtained as the
solution of a minimax programming problem. An axiomatic characterization of the cautious solution is provided, when some requirement on the bargaining set are assumed. An
extension of the classic model of firm-union negotiation, which includes situations where
uncertainty about the consequences of the agreements has to be taken into account, is
analyzed in this framework.
Keywords: Bargaining, Multi-Scenario, Solution Concepts.
136
MO4C (invited)
Characterization and Properties of Approximate
Proper Solutions in Vector Optimization Problems
Huerga , Lidia (*)
Jiménez, Bienvenido
UNED
Novo, Vicente
Gutiérrez, César
Universidad de Valladolid
We present a new concept of approximate proper solution for a constrained vector optimization problem defined on a locally convex Hausdorff topological linear space. We
characterize this set of approximate proper solutions through approximate solutions of
associated scalar optimization problems by assuming generalized convexity assumptions
and we study its properties.
Keywords: Linear Scalarization, Nearly Subconvexlikeness, Proper Epsilon-Efficiency,
Vector Optimization.
137
FR3G (invited)
Classification of Disasters Effects and Humanitarian
Decision-Makers’ Criteria
Rodriguez, Tinguaro (*)
Montero, Javier
Vitoriano, Bego˜
na
Universidad Complutense de Madrid
A fully precise numerical initial assessment of disasters’ effects on people as well as on
their social and natural environment is unrealistic in the time-pressured, highly uncertain
decision context taking place just after a disaster strike. Instead of a numerical evaluation, here it is considered that it is rather more plausible and realistic to classify the
severity of the consequences of a disaster in terms of the relevant scenarios for NGO’s
decision makers. However, some features of that context, as the natural ordering of the
consequences’ gravity or the need of avoiding underestimation risk, entail the necessity of
considering and assuming an structure over the set of classes, somehow modeling those
features inside of the classification model.
Keywords: Classification, Fuzzy Sets, Humanitarian Logistics.
138
TU5C (contributed)
Climate Change - a Challenge for MCDA?
Belton, Valerie (*)
University of Strathclyde
The UNEP website describes climate change as one of the major challenges of our time,
giving rise to impacts that are global in scope and unprecedented in scale. It advocates
anticipatory action today as future adaptation will become more difficult and costly. The
UNEP sponsored MCA4climate initiative provides a framework to assist governments
in preparing climate change mitigation and adaptation strategies. At the heart of the
framework is a generic MCDA model which provides the basis for the evaluation and
prioritisation of potential policy actions. The paper will describe the process of model
development, which drew on in depth expertise in 12 mitigation and adaptation themes
and incorporated 3 illustrative case studies, and discuss the challenges to MCDA posed
by this ambitious initiative. A collaborative project, led by the INCAE Business School
in Costa Rica, is currently underway to use the MCA4climate framework to explore the
adaptation of the agricultural sector in Peru; progress, challenges faced and hopefully
successes achieved with be reported at the conference.
139
TU4B (contributed)
Combined Dominance-based Rough Set Approach and
Fuzzy DEMATEL for Analyzing the Adoption
Intention of Tourist Guide Mobile Applications
Chin, Yang-Chieh (*)
Asia University
The multiple capacity resources and advances of smartphones enhance the demand of
mobile applications. Therefore, mobile applications are one of the most rapidly growing
segments of the software market. The goal of this project is to gain insight into the factors
that affect user intention to adopt tourist guide mobile applications. The present project
uses an extended technology acceptance model and focuses on combining innovativeness,
intrinsic motivation (i.e., perceived enjoyment) and extrinsic motivation (i.e., perceived
ease of use and perceived usefulness) to explain usage intentions for tourist guide mobile applications. Firstly, a dominance-based rough set approach (DRSA), a rule-based
decision-making technique, is used to determine the adoption intentions associated with
decision rules in toruist guide mobile applications. The second phase of the project uses
Fuzzy Decision Making Trial and Evaluation Laboratory (Fuzzy DEMATEL) technique
for analyzing the relationship among factors of adoption intention in tourist guide mobile
applications. These findings help to address real-world dependence and feedback problems and can help organizational decision-makers in planning, evaluating and executing
the use of tourist guide mobile applications. Practical and research implications are also
offered.
Keywords: Analytic Network Process, DEMATEL, E-Commerce, Fuzzy Integral, Rough
Sets, Service Quality.
140
TU5B (contributed)
Combining MCDA and Risk Analysis: Dealing with
Scaling Issues in the Multiplicative AHP
Barfod, Michael Bruhn (*)
Technical University of Denmark
Salling, Kim Bang
Van Den Honert, Rob
Macquarie University
This paper proposes a new decision support system (DSS) for applying risk analysis and
stochastic simulation to the multiplicative AHP in order to deal with issues concerning the
progression factors. The multiplicative AHP makes use of direct rating on a logarithmic
scale, and for
√ this purpose the progression factor 2 is used for calculating scores of alternatives and 2 for calculation of criteria weights when transforming the verbal judgments
stemming from pair wise comparisons. However, depending on the decision context, the
decision-makers aversion towards risk, etc., it is most likely that the decision-makers have
varying progression factors in their perception of the scales. This potential variability
in progression factors implies a form of uncertainty in the DSS which can be modelled
by stochastic simulation. With reference to previous work, which proposes to conduct
sensitivity analysis using a short, natural, and long scale, the proposed paper suggests
a new approach in order to capture the influence from the progression factors in terms
of including probability distributions. Herein, the uncertainty both with regard to the
scale and the inherent randomness from the factor will be captured by probabilistic input
and output distributions. Provided that each alternative and criteria under consideration
are independent it can be assumed that the embedded uncertainty from the progression
factors remains the same. The result will then be an interval estimate (instead of a single
point estimate) for each alternative’s final scores. This might lead to overlapping intervals
of scores which may be interpreted as possible rank reversals. Thus it will be possible to
calculate the probability of overlapping due to the uncertainty in the progression factor
for any given set of pairwise comparisons. Simulations on real-life data sets are used to
form the conclusions and perspectives for the proposed DSS.
Keywords: Analytic Hierarchy Process, Decision Analysis, Decision Support Systems,
Risk Analysis.
141
FR2B (contributed)
Comparative Analysis and Evaluation of
Multi-Criteria Decision Making Methods
Kou, Gang (*)
UESTC
Ergu, Daji
Southwest University for Nationalities
Saaty, Thomas L.
Katz Graduate School of Business
Real life decision problems usually involve multiple and conflicting criteria. Many multicriteria decision making (MCDM) methods have been proposed to deal with the measurement of tangible/intangible conflicting criteria and with the measurement of the alternatives of a decision with respect to these criteria in order to choose the best one between
them. However, the existence of many MCDM methods becomes a decision problem, and
decision makers may be uncertain about which one to use. Thus it is also important to
compare and evaluate the existing MCDM methods, and explore if there are logical or
mathematical, social or practical reasons why one method is better than another. This paper first evaluates a simple decision choosing the best car among three cars, using several
(more than ten) MCDM methods including the AHP, Utility, VIKOR, PROMETHEE II,
DEMATEL, ELECTRE, ARAS, COPRASS, MACBETHE, UTA etc. Based on this validation, we are further concerned with the comparison and evaluation of these different
methods on a wide set of criteria such as 1) Good and complete understanding of the
importance of a decision; 2) The Creativity, Generality, Breadth and Depth of Decision
Structure ; 3) Faithfulness of judgments (Individual and Group); 4) Scale of measurement; 5) The way of obtaining weights; 6) Measurement of intangibles; 7) Applicability
to conflict resolution; 8) Social political group decision making (GDM); 9) Identifying
incompatible decision maker in the context of GDM; 10) How to combine judgment; 11)
How to deal with uncertainty; 12) Applicability to feedback; 13) Validity of the outcome
(the ability to predict). The goal of this paper is to analyze the similarities and differences
of each MCDM method and summarize the common characteristics of each to create a
general framework for multi-criteria decision making methods. The analyzed results can
help decision makers to choose the appropriate multi-criteria decision making method to
make a valid decision.
Keywords: Comparative Analysis, Conflicting Criteria, Multi-Criteria Decision Making.
142
TH2D (invited)
Comparing Fuzzy Goal Programming Approaches for
Collaborative Supply Chain Master Planning
Diaz-Madro˜
nero, Manuel (*)
CIGIP-UPV
Mula, Josefa
Universitat Politècnica de València
Peidro, David
The objective of this paper is improving the proposal by Selim et al. (2008) Collaborative
production-distribution planning in supply chain: A fuzzy goal programming approach.
Transportation Research Part E: Logistics and Transportation Review, 44(3), 396-419.
In the context of a supply chain collaborative planning problem, the authors differentiate
that an approach based uniquely in the weighted sum of the decision maker satisfaction
levels can be corresponded to a centralized collaborative perspective while an approach
based on the min operator can be corresponded to a decentralized one. In this paper, we
compare the original proposal by Selim et al. (2008) to other representative fuzzy goal
programming approaches for decentralized and/or centralized master planning problem
in a real-world ceramic tile supply chain. This work has been funded by the Universitat Politècnica de València projects: ‘Material Requirement Planning Fourth Generation
(MRPIV)’ (Ref. PAID-05-12).
Keywords: Collaborative Planning, Fuzzy Sets, Goal Programming, Meta-Goal, Production, Supply Chain Management.
143
TU5E (contributed)
Comparing the Scalarization Methods for Bi-Objective
Assembly Line Scheduling Problem
Akyol, Emine (*)
Anadolu University
Sara¸c, Tugba
In this study, bi-objective assembly line scheduling problem in a plastic part production
process is considered. The objective functions are minimized the sum of tardiness and the
sum of earliness. The objective functions are scalarized by using well-known scalarization
methods in the literature and obtained results are compared.
Keywords: Assembly Line, Scalarization, Scheduling.
144
TH3G (invited)
Comparison between Continuous and Discrete
Multicriteria Decision Methods applied to Lands Use,
Conservation, Management and Planning on
Hydro-Basin in Cordoba Province (Argentina)
Grau, Juan (*) Andina, Diego Antón, José Tarquis, Ana
Cisneros, José Manuel
Universidad Nacional de R´ıo Cuarto
The authors have attempted to study the similarities and differences between the application of continuous and discrete Multi-criteria methods to the maintenance and basin
hydrological management and Planning, taking into account the land use of the influence
area and its characteristics. Firstly, the authors have obtained data on soils and on hydrology of the study area, and evaluated the systems of soil uses and actions that could be
recommended considering the relevant aspects of the study area. Several characteristics
such as: crops, erosion, employment, economic sustainability, etc. have been considered.
Later on, Decision Support Systems (DSS) with Mathematic tools for planning of defences and uses of soils in these areas have been applied, and these were conducted here
to use multi-criteria decision models (MCDM), first discrete, for the more global views
about soil conservation and hydraulic management actions and to chose among global
types of use of soils, and second continuous, in order to evaluate and optimize combined
actions, compare the results obtained with discrete methods and to see if they could provide another significant aspects of the problem, such as, control of erosion and hydraulic
management. For that, they used discrete methods for the different sub-basins, ELECTRE, weighted and original PROMETHEE and AHP methods with a system of criteria
grouped as environmental, economic and social, to obtain a better view on the general
aspects for the middle or long term planning for these areas. In relation to continuous
methods, they used Weighted and Lexicographic Goal Programming and Compromise
Programming. This contribution shows the results obtained, the discussion about them
and the use of one or another, and finally the recommendation to apply MCDM to hydrobasin Management and Planning.
Keywords: Applications of MCDM, Case Study, Environment, Multi-Criteria Decision
Making.
145
TU4C (contributed)
Comparison of Two Methods for Developing a
Multicriteria Evaluation System to Assess Animal
Welfare
Mart´ın Fernández, Paula (*)
CAU Kiel
Buxadé, Carlos
Polytechnic University Madrid
Krieter, Joachim
CAU Kiel
The aim of this paper is to develop a multicriteria evaluation system to assess animal
R distinguishes four welfare criteria (Good feeding, Good houswelfare. Welfare Quality
ing, Good health and Appropriate behaviour). A farm receives one score for each welfare
criteria. Nine farms were used as an example illustrated on fattening pigs. Multiattribute utility theory was used to produce an overall value of welfare starting from the
data regarding the four main criteria. The utility functions and the aggregation function
were constructed in two separated steps. First, utility functions for each criterion were
determined in two different ways, by using the standard sequences method (SS) and by
using the MACBETH software. In the second step, criteria were aggregated. As aggregation functions, the weighted sum (WS) and the Choquet Integral (CI) were used. The
WS was implemented with different weights to analyse the sensitivity of the method. A
progressive interactive approach was used in the CI determination to define the relative
importance of the different criteria and the allowance or not of compensation between
criteria. Results showed that when using the WS the ranking derived from the utility
functions calculated by the MACBETH approach was more consistent with the preferences of the decision maker (DM) than the ranking derived from the SS method, but, for
both methods the weights have a high influence on the results, having several ranking
reversal when the weights are slightly modified. From the CI results we could notice that
there were interactions between the criteria, therefore, the use of the WS in this context
is not adequate due to the fact that it does not take into account these interactions. The
utilities derived from MACBETH suited better with the progressive interactive approach
proposed to calculate the CI, allowing to model better the preferences of the DM regarding the different importance of the criteria and the interaction between them.
Keywords: Animal Welfare, Choquet Integral, Criterion Importance, Multi-Attribute
Utility Theory, Sensitivity Analysis, Weighted Sum.
146
TU5F (contributed)
Computing Nadir Point for Multiobjective Discrete
Optimization Problems
Kirlik, Gokhan (*)
Koc University
Sayin, Serpil
Koc University
Optimization problems with more than one objective function is called a multiobjective
optimization problem (MOP). In this paper, we investigate the problem of finding the
nadir point for multiobjective discrete optimization problems (MODO). MODO is a special case of MOP where all variables are discrete. The nadir point is characterized by the
componentwise worst values of objective functions among nondominated points of a MOP.
Obtaining the nadir point is generally a hard problem. Along with the relatively easy to
obtain ideal point, the nadir point is an important element of MOP, because these points
define lower and upper bounds of the efficient set. In fact, there are some methods that
require the nadir point as input. Hence, determination of the nadir point is an important
concept in MOP. We present a new algorithm to compute nadir values for MODO with
p objective functions. The proposed algorithm is based on an exhaustive search of the
(p-2)-dimensional space for each component of the nadir point. We partition this space
into (p-2)-dimensional rectangles to search the space entirely. During the search, epsilonconstraint scalarization is used to obtain efficient solutions. Since the optimal solution
of the epsilon-constrained method is weakly efficient, we use second stage to eliminate
those solutions. The method guarantees to find the nadir point in a finite number of
iterations. We compare our algorithm with an earlier one from the literature. Both algorithms are implemented in C++. Subproblems are solved by using IBM CPLEX 12.4.
These algorithms are tested on the randomly generated multiobjective knapsack problem,
assignment problem, and integer linear programming problem instances. Our algorithm
is able to obtain the nadir point for relatively large problem instances with up to fiveobjectives.
Keywords: Multi-Objective Optimization, Nadir Point.
147
MO3G (invited)
Concept and Empirical Verification of the Benefit of
Kunz, Reinhard (*)
University of Bayreuth
Siebert, Johannes
Value-focused Thinking is attracting more and more attention in literature and in practice.
Its great popularity indicates that a decision maker can benefit from using Value-focused
Thinking. An extensive literature review shows that previous studies concentrate on the
results and not on the process of Value-focused Thinking. León (1999) found out that
using Value-focused Thinking leads to a more extensive hierarchical structure of objectives in comparison to Alternative-focused Thinking. Thereupon the structure generated
by Value-focused Thinking is evaluated at least equal or superior by experts. Selart and
Johanson (2011) explore that Value-focused Thinking seems to have a positive impact on
the quality of ideas in terms of creativity and innovativeness. In this paper we present
a concept to measure the benefits using different perspectives and considering different
aspects of Value-focused Thinking. Several constructs and valid management scales are
identified and empirically tested in a pre-study measuring the benefits of Value-focused
Thinking.
Keywords: Decision Analysis, Measurement, Problem Structuring, Value Focus Thinking.
148
MO2D (contributed)
Constructing Ecological Value Functions for River
Rehabilitation: Expert Elicitation with Different
Aggregation Schemes
Lienert, Judit (*)
Eawag
Langhans, Simone
Eawag
Reichert, Peter
Eawag
River rehabilitation aims to reduce human impacts on e.g. biodiversity and ecosystem
services, but is expensive and entails difficult trade-offs. Although suitable, MCDA has
not received much attention as a tool to support selecting appropriate rehabilitation measures. Many applications were simplified (few objectives, linear value functions, additive
aggregation model). Additional challenges include the best choice of attributes for valuation. We propose a comprehensive ecological value function to assess the biological and
hydromorphological state of smaller rivers in Central Europe. In 6 one-to-one interviews
with river ecologists, we developed a large objectives hierarchy, elicited value functions
for over 60(!) attributes, scaling constants (weights), and each ecologist’s risk attitude.
We discussed implications of an additive model and suggest alternative schemes (minimal, multiplicative, cobb-douglas, mixed aggregation). We set up hypothetical scenarios
(“decision alternatives”) for attribute states and calculated the overall value of the river’s
ecological state, based on each expert’s preferences. We then used different aggregation
schemes, and assumptions about scaling (e.g. equal weights), or value functions (linear)
to test the sensitivity of the result. We discuss that although hierarchical multiplicative
aggregation proved satisfactory in the interviews, mixed aggregation may better fulfill
desired properties of an ecological value function. The large number of attributes can be
turned into an advantage with hierarchical aggregation from lower to higher objectivelevels. It increases flexibility (application to various river types and spatial scales) and
allows selecting preferred attributes. If more attributes are used, the statistical significance of branches increases without changing weights. Our findings may be relevant for
many practical MCDA-applications with extensive objectives hierarchies and non-linear,
and/or non-compensatory attributes.
Keywords: Aggregation Schemes, Decision Maker’s Preferences, Elicitation, Environmental Decision Making, Multi-Attribute Utility Theory, Water Resources Management.
149
MO3G (invited)
Creating a Balanced Scorecard by using Value-Focused
Thinking
Muetterlein, Joschka (*)
Kunz, Reinhard
Siebert, Johannes
Since its introduction by Kaplan and Norton in 1992 the Balanced Scorecard has started a
remarkable triumphal march. These days the majorities of the companies in the Dow Jones
use the Balanced Scorecard to measure and control their performance. The widespread
use manifests itself also through thousands of articles and books dealing with extensions
and further developments. It is therefore surprising that the procedure how to create a
Balanced Scorecard is given only little attention. In literature there are often only practical guides which describe in detail how in workshops of the top management and of
the lower management have to be organized to define the vision, mission and strategy of
the company. In contrast their systematic implementation in different perspectives and
consistent translation in performance indicators under consideration of cause-and-effectnetworks is only insufficiently addressed. We propose an innovative procedure to identify
and structure the relevant objectives using Value-focused Thinking (Keeney 1992). Fundamental objectives can be used to create the perspectives in a Balanced Scorecard.
Instead of cause-and-effect-networks means-end-networks represent the relations between
the performance indicators within or in between the perspectives. This procedure ensures
a systematic and consistent implementation of the perspectives and performance indicators. We illustrate our procedure with a case study.
Keywords: Case Study, Decision Analysis, Measurement, Problem Structuring, Value
Focus Thinking.
150
MO1A (plenary)
Creating, Structuring, and Using Multiple Objectives
Keeney, Ralph (*)
Duke University
For an important decision, a set of objectives provides a logical basis for all of the activities
necessary to solve that decision. The first part of this presentation discusses the difficulties of identifying new objectives, and then presents procedures that significantly help in
creating an appropriate set of objectives for any decision. Then concepts and procedures
to structure these objectives are outlined. The second part of the presentation describes
several uses of the objectives. These include framing a stated, or even just perceived,
decision problem for a subsequent analysis, creating alternatives to solve a decision, recognizing proactive decision opportunities that would contribute to the achievement of sets
of objectives, prioritizing objectives by constructing a multiple-objective objective function, and evaluating alternatives proposed for a decision to identify the best one or better
ones and eliminate poor choices. All of the procedures to create the structure multiple
objectives and to use them are illustrated with personal, business, and public applications.
Keywords: Applications of MCDM, Creating Alternatives, Identifying Objectives, Organizing Objectives, Prioritizing Objectives, Structuring Decisions.
151
FR2F (invited)
Cross Entropy for Combinatorial Optimization
Problems with Linear Relaxations
Molina Luque, Julián (*)
Hernández-D´ıaz, Alfredo G.
Pablo de Olavide University
Laguna, Manuel
University of Colorado
While the cross entropy methodology has been applied to a fair number of combinatorial
optimization problems with a single objective, its adaptation to multiobjective optimization has been sporadic. We develop a multiobjective optimization cross entropy (MOCE)
procedure for combinatorial optimization problems for which there is a linear relaxation
(obtained by ignoring the integrality restrictions) that can be solved in polynomial time.
The presence of a relaxation that can be solved with modest computational time is an
important characteristic of the problems under consideration because our procedure is
designed to exploit relaxed solutions. This is done with a strategy that divides the objective function space into areas and a mechanism that seeds these areas with relaxed
solutions. Our main interest is to tackle problems whose solutions are represented by
binary variables and whose relaxation is a linear program. Our tests with multiobjective
knapsack problems and multiobjective assignment problems show the merit of the proposed procedure.
Keywords: Cross Entropy, Evolutionary Multi-Objective Optimization, Multi-Objective
Assignment, Multi-Objective Combinatorial Optimization, Multi-Objective Knapsack.
152
TU2F (contributed)
Dealing with Migratory Flows under Uncertainty: a
Multi-objective Optimization Approach
Campoy-Mu˜
noz, Pilar (*)
Universidad Loyola Andaluc´ıa
Garc´ıa-Alonso, Carlos R.
Universidad Loyola Andalucia
Salazar-Ordo˜
nez, Melania
Universidad Loyola Andaluc´ıa
Nowadays, managing migratory flows has become one of the most relevant challenges for
politicians in the recipient countries. However, the estimation of those inflows has turned
into a complex problem given both the plethora of variables driving them and the multiple interactions among drivers.This paper presents a tool to help governments forecast
immigration flows by combining simulation and fuzzy logic techniques. This approach
allows dealing with the uncertainty arisen from the scarce data available, but also results
into a multi-objective optimization problem regarding to the fitting of the model parameters. To tackle it, an evolutionary algorithm is designed for this task. The proposal is
used to evaluate the Ecuadorian inflows to Spain during the last decade. The results are
consistent with those previously obtained by expert-knowledge elicitation while providing
some useful insights for national migratory policy design.
Keywords: Fuzzy Logic, Genetic Algorithms, Immigration, Monte Carlo Simulation,
153
MO5H (poster)
Decision Making using Immune Algorithm for
Increasing of Level Crossing Capacity
Gorobetz, Mikhail (*)
Riga Technical University
The purpose of this research is multi-objective optimization of level-crossing traffic capacity by decision making using immune algorithm. There are conflicts between different
transport types in cities, where road and rail transport is mixed. Road transport is forced
to wait at level crossing, while train is passing. Current level-crossing control system
does not take in account criteria of train location and speed. And the existing schedule
shows that opposite directions trains do not pass the level-crossing simultaneously. By
these reasons, a level-crossing is closed longer than might be. Therefore some solutions to
optimize the schedule and decision making for safe and efficient closure of level-crossing
are necessary. Hypothesis, that the existing schedule adjustment and embedded devices
may reduce the time of level-crossing in a closed position, is defined. In this research the
mathematical model of level-crossing railway and road transport flows is created taking in
account specifics of railway control and signalization systems and target function is developed for multi-objective optimization by criteria of road transport idle time, simultaneity,
safety and shift from the original schedule. Dual population immune algorithm is used for
schedule optimization. Also genetic algorithm has been used with the same target function and model to compare the results. The computer model as well as the 24h schedule
of real existing problematic level-crossing with heavy transport traffic has been optimized
and evaluated. Statistics is collected from various computer experiments with genetic
and immune algorithms. Developed transport system control algorithm allows increasing
the level crossing capacity up to 49% for computer model and up to 25% for real levelcrossing. The research leading to these results has received funding from the ARTEMIS
Joint Undertaking under grant agreement n◦ 295373 and from Latvian Academy of Science
and Ministry of Education and Science.
Keywords: Immune Algorithm, Multi-Objective Optimization, Traffic.
154
TU3G (contributed)
Decision Support for Multi-Attribute Multi-Item
Reverse Auctions
Karakaya, G¨
ul¸sah (*)
Koksalan, Murat
In this study, we address multi-item auction problems in a multi-attribute, multi round
reverse auction setting. In single-attribute auctions there is one attribute, typically the
price. In multi-attribute auctions there are additional attributes and the comparison of
bids is not straightforward. Multi-item auctions also bring additional complexity over
single-item auctions. In the single-item auction, the winning bidder supplies the item
with the committed attribute values. On the other hand, in multi-item case it is not
so trivial to determine the winning bidders. In such auctions, generally bidders offer a
combination of items – a bundle – they wish to supply. They specify the attribute values of the bundle and make their bids. Multi-item auctions are known as combinatorial
auctions. The auction process continues several rounds. In each round, we provide the
buyer with a set of efficient bid combinations. The buyer then chooses the provisional
winners whose bids collectively provide all the required items. We estimate preference
information from the buyer’s past choices and provide this information to the bidders.
The bidders update/improve their bids in order to become/stay competitive. The aim
of the developed interactive approach is to have the more competitive bidders eventually
end up winning the auction. We tried the algorithm for underlying linear preference functions. We also adapted the approach as a heuristic for the case the buyer has a nonlinear
preference function. There, we locally approximate the buyer’s preference function with
a linear function. The approach worked well for the problems we solved.
Keywords: Auctions/Bidding, Decision Support, Interactive Methods, Multi-Criteria
Decision Making.
155
TH4B (contributed)
Decomposing Value Creation when Assessing
Investments: a Multi-Criteria Approach
Guerrero-Baena, Mar´ıa Dolores (*)
University of Córdoba
Although traditional financial techniques based on the NPV and the IRR are widely used
by corporate officers in order to support decision-making for capital budgeting, many
researchers have criticized them for failing to recognize the real value generated by new
investments, since they ignore qualitative impacts that also add value to the firm. In this
paper, we aim at partially solving this shortcoming by developing a novel approach to
appraising investments considering the overall value created by the new assets, which can
be decomposed into financial and nonfinancial value, the latter stemming from intellectual
capital. To do so, a methodology based on the Analytic Hierarchy Process (AHP) is proposed, which allow assessing more accurately the value creation of the different alternative
investments and supporting a more adequate decision-making. The method proposed is
empirically illustrated by a case study taken from the agrifood sector in order to check
its operational feasibility, where an investment in a food quality control technology is
considered.
Keywords: Analytic Hierarchy Process, Capital Budgeting, Intellectual Capital.
156
TU2B (contributed)
Deriving Priority Weights from Pairwise Comparison
Matrices under Different Rationality Scenarios
Romero, Carlos (*)
González-Pachón, Jacinto
Tecnical University of Madrid
The final purpose of the pairwise comparison method is to deduce a set of numerical values representing the relative importance of different objects for a decision maker (DM).
In this paper, this task is tackled by considering different scenarios of rationality. In the
literature this problem has been addressed by two directions: when we state “a priori”
hypothesis about the rationality of the DM, or when the rationality study is made “a
posteriori”; i.e., when a complete pairwise comparison matrix has already been obtained.
In the first direction, we consider three different scenarios according to the level of rationality underlying the information provided by the DM: rational decision-maker, partially
rational decision-maker and irrational decision-maker. For the second direction, we consider again three different approaches: normative, descriptive and a compromise between
normative and descriptive approaches.
Keywords: Distance-Based Methods, Goal Programming, Pairwise Comparison, Preference Modeling.
157
TH4C (contributed)
Design Safer and Greener Road Projects by using a
Multi-Objective Optimization Approach
Sarrazin, Renaud (*)
Universite libre de Bruxelles
De Smet, Yves
Université libre de Bruxelles
For many years, considering sustainable development and improving road safety have been
two majors concerns in mobility and transport policies in Europe. Since 2001, the European Commission (EC) had published several reports about European transport in which
actions have been taken to improve the global level of safety on the European road network
by 2020. In 2010, the Greening Transport Package had been published about strategies to
apply in order to strive for a transport system more respectful of the environment. But to
date, the assessment of the road safety of an infrastructure is essentially based on the evaluation of statistical data that offers the administration a support in the identification of
the roads with high accidents concentration – or black spots. These methods are based on
a reactive approach. However, to meet the objectives of the EC of improving road safety
and considering sustainable character of the road transport infrastructure, it has become
essential to develop new preventive and innovative tools. Consequently, we had initiated
this research project with the aim of developing a multicriteria analysis methodology to
carry out an integrated and preventive assessment of the road safety performances and
the sustainable aspects of road projects at the design stage. At first, we have developed
an innovative concept of sustainable safety that we transformed into a complete set of
criteria. And then, we have studied the interest of using a multi-objective optimization
(MOO) methodology to conduct a pertinent analysis of our design problem. This work
illustrates the efficiency of MOO in design space exploration and the development of interesting solutions. Moreover, the use of MOO allows us to deal with several criteria and
then to design alternatives more adapted to the stakes and the characteristics of a road
project. In the end, these results would help the design engineers in the development and
the selection of safer and greener solutions.
Keywords: Multi-Objective Optimization, Road Safety, Sustainability Assessment.
158
TU4E (contributed)
Determinants of Experienced Utility: Laws and
Implications
Sarin, Rakesh (*)
UCLA
Baucells, Manel
Rand Corporation
Satisfaction in experiencing the future depends on decisions made today. We consider
six well-known psychological laws governing satisfaction. The laws capture habit formation, social comparison, and satiation. We show it is possible to formalize these laws by
means of a utility model, and to derive implications from the laws: wanting vs. liking,
crescendo, recharge periods, variety seeking, and craving. The discussion combines mathematical propositions, experimental findings in psychology, and time-honored wisdom. We
discuss how the sixth law, presentism, may lead to incorrect predictions of experienced
utility and suboptimal life-balance choices.
Keywords: Decision Analysis, Utility Theory.
159
FR3G (invited)
Development of an Innovative Environmental
Education Program for Urban Solid Waste
Management through Multi-Criteria Decision Methods
(MCDM)
Martin, Rodrigo
UNED
Soletti, Alfredo
Universidad Católica de Córdoba
This contribution shows the design of an innovative program of Environmental Education for the integrated management of urban solid waste (USW) with the aid of discrete
multi-criteria decision methods. In the development and elaboration of strategies, the
authors have taken into account all the levels to which it is addressed, the socio-economic
conditions of the area. They have also considered the main objective of creating a culture
oriented to progressive change of habits and practices in the efficient management of USW
from the place of origin up to its final disposal through its storage and transport.
Keywords: Applications of MCDM, Multi-Criteria Decision Making, Outranking Methods.
160
MO3C (contributed)
Development of Interactive Support System Using
Spreadsheet based on Multiattribute Utility Analysis
Tomohiro, Hayashida (*)
Hiroshima University
Ichiro, Nishizaki
Mitsuhiro, Imai
Multiattribute utility analysis (MAUA) (Keeney and Raiffa, 1976) is a multi-criteria decision method, such that preference structure of a decision maker is quantitatively-modeled
by multiattribute utility function. The function consists of multiple weighted single attribute utility functions, the weight of each single attribute is indicated by a scale constant.
A decision support system based on MAUA “MIDASS” is developed by Seo et al. in 2007.
This system makes analysts avoid complicated calculations under MAUA. MIDASS consists of several applications having particular functions for identifying the single attribute
utility functions and the scale constants, calculation of expected utility value of each alternative, and database management. It can operate on windows OS. MAUA is an effective
approach for a wide range of decision problems, however, MIDASS is supposed that an
expert user at whole procedure of MAUA. Due to complexity of operations of MIDASS,
the software is difficult for a user who is not an expert. In this study, we develop an
interactive decision support system which runs on spreadsheet with basic functions and
easy to operate for multiattribute utility analysis.
Keywords: Decision Support, Multi-Attribute Utility Theory, Multiplicative Form.
161
FR3G (invited)
Development of Mathematical Multicriteria Methods
to Aid to Elaborate Plans of Rural Sustainable
Development in Areas With Aborigines Communities
Antón, José (*) Andina, Diego Grau, Juan Tarquis, Ana
Rubio, José Luis
The purpose of this paper is to present the development of mathematical models based
on discrete multi-criteria methods to be used in the development of rural management
plans in municipalities with social integration problems, especially when aborigine communities are settled in their territory. In these municipalities there also exist problems
of natural hazards and environmental erosion, desertification and adaptation to possible
climate change. All this is accompanied by serious economic shortcomings that move to
lack of planning scenarios for sustainable economic development of the territory. So that
the design of a method to aid decision making in the development plans to be carried out
by the municipalities and regions is of high importance in most of the Latin American
countries.
Keywords: Environment, Multi-Criteria Decision Making, Rural Planning.
162
FR3G (invited)
Discussion of the Results Obtained in Several Real
Applications using PROMETHE I and II versus
Weighted PROMETHE and other Outranking
Methods
Colombo, Federico De los R´ıos, Lisandro
UCASAL
The authors have carried out a deep analysis of the results obtained in these four real
applications: 1) Optimization of an erosion control integral plan in Chaco area in Salta
Province (Argentina), 2) Territorial planning in La Colacha river basin (Córdoba Province,
Argentina), 3) Election of water resources management entity, 4) Election of waste management system in an Andean village using several discrete multi-criteria decision methods. PROMETHEE I, II and a PROMETHEE modified by the authors named weighted
PROMETHEE have been used. Also, others outranking methods as ELECTRE I and
AHP have been tested. This contribution shows the reasons why the weighted PROMETHEE should be used in place to I and II for some kind of applications.
Keywords: Applications of MCDM, Outranking Methods, PROMETHEE.
163
TH2E (invited)
Distributed Computation of Pareto Points in
Multiobjective Programming
Wiecek, Margaret (*)
Clemson University
Dandurand, Brian
Clemson University
Complex systems engineering design has motivated the development of optimization methods dealing with multiple design disciplines, with each discipline generating multiple design criteria. The complexity is reflected in the system composition of subsystems and
components. Multiple disciplines originate from various science and engineering areas,
such as structures or dynamics, which interact with each other within the design process,
while multiple criteria are required to describe the system performance. Interaction among
the design processes of the multiple disciplines is necessary due to the natural overlap and
influence that the underlying scientific principles have on one another. To address the
needs of engineering design, the design process is modeled as the Multidisciplinary Multiobjective Optimization Problem (MMOP), and interdisciplinary communication protocols
and algorithms for distributed computation of Pareto points of the MMOP are developed.
The MMOP is presented as distinct and nonintegrable multiobjective subproblems corresponding to the interacting disciplines. The protocols govern the interactions between the
subproblem solvers during the design process, enable specialization, guarantee stability of
the system-wide design process, and coordinate the computation of system-wide Pareto
designs. The algorithms make use of two Gauss-Seidel-type decompositions known as
Block Coordinate Descent (BCD) and the Alternating Direction Method of Multipliers
(ADMM), and a Lagrange multiplier method known as the method of multipliers (MM).
BCD and ADMM are applied to MMOPs whose decompositions originate in the decision
and objective space, respectively. The convergence properties of the algorithms, which
had been established in the single objective case, are examined in the multiobjective setting. Mathematical and engineering examples are included.
Keywords: Block Coordinate Descent, Decomposition, Lagrangian Duality, Multi-Objective Optimization.
164
MO5H (poster)
Distribution of Federal Funds to Mexican Subnational
Governments: Identifying Tools to Enhance MCDA
Process in the Public Security Sector
Vergara-Maldonado, E. Ivonne (*) Sánchez-Guerrero, Gabriel
National Autonomous University of Mexico
This research concerns the identification of tools and methods that can be integrated into
a framework to aid the process of decision making related to the distribution of federal
funds to the Mexican states, in the public security sector. Since violence and crime have
risen over the past decade, the Federal Government increased considerably the annual
federal budget in order to allocate public funds across the national territory to prevent,
combat and penalize the crime. One of these resources is the Fund of Contributions for
Public Security, which contributed with about 7 billion of Mexican pesos in 2012. This
budget is distributed to 32 states based on a model with five main criteria, a considerable
number of sub-criteria, and weighted sums. Since its establishment in 1997, this model
has been frequently questioned and modified in their criteria and weights. The last change
was in 2010, and it was derived from a participatory process with stakeholders from the
National System of Public Security; where the great number of variables in the social
phenomenon of crime, legal framework, political constraints and a wide variety of interests due the complexity of the system, gave as a result an unfeasible model, which needed
an urgent correction in order to meet the restrictions from the main decision-maker, the
National Council of Public Security. This research analyzes this situation in an agreement/certainty framework (Patton, 2011) and identifies the appropriate multi-criteria
approach to manage this type of decision making processes (Munda, 2004). Finally, using the typology of systems methodologies related to problem context (Flood & Jackson,
1995; Jackson, 2003), this work suggests a few methods and tools that can be integrated
into a MCDA framework, in order to enhance the viability and acceptance of the design
and results of the model to distribute this public fund.
Keywords: Federal Funds Distribution, Multi-Criteria Decision Aid, Multi-Criteria Decision Analysis, Multi-Methodology.
165
MO5H (poster)
DR-MOEA/D: A new Double Reference Point
Approach of MOEA/D Based in Aspiration and
Reservation Levels
Saborido-Infantes, Rubén (*)
Alba, Enrique
Luque, Mariano
Evolutionary multiobjective optimization (EMO) methodologies have attained wide popularity for solving practical optimization problems, due to their ability to find a representative set of Pareto-optimal solutions. The multiple criteria decision making (MCDM)
literature also offers many possibilities to carry out this task involving user preferences,
which can be supplied in different forms. We propose a variant of the popular MultiObjective Evolutionary Algorithm based on Decomposition (MOEA/D), called DR-MOEA/D,
for finding a preferred well distributed reduced set of solutions to approximate a region of
interest, instead of the complete Pareto-optimal frontier, by incorporating preference information of the decision maker based on the information included in two reference points
(aspiration and reservation). Using these reference levels, we generate a well distributed
set of reference points in the region of interest, covering the objective space from the
aspiration level. Considering these reference points, an achievement scalarizing function
(ASF) is used to project solutions on the non-dominated set. DR-MOEA/D approximates
the region of interest delimited by two reference points (aspiration and reservation), and
gets a set of Pareto-optimal solutions, if the aspiration point is unachievable and the
reservation point is achievable. In other cases, when there no Pareto-optimal solutions
exist in the region of interest, the algorithm projects this region onto the Pareto-optimal
set. The method is validated with several standard problems of the literature (families
CTP, ZDT, DTLZ and WFG).
Keywords: Achievement Scalarizing Functions, Evolutionary Optimization, Multi-Objective Optimization, Pareto Optimal Solutions, Preference-Based Evolutionary Algorithms.
166
WE1G (contributed)
Dynamic Multi-Objective De Novo Linear
Programming
Fiala, Petr (*)
University of Economics Prague
Mathematical programming under multiple objectives has emerged as a powerful tool to
assist in the process of searching for decisions which best satisfy a multitude of conflicting
objectives. There are a number of distinct methodologies for multi-objective decisionmaking problems that exist. In Multi-objective Linear Programming (MOLP) problems
it is usually impossible to optimize all objectives in a given system. Trade-off means that
one cannot increase the level of satisfaction for an objective without decreasing this for
another objective. Trade-offs are properties of inadequately designed system a thus can
be eliminated through designing better one. Multi-objective De Novo linear programming
(MODNLP) is problem for designing optimal system by reshaping the feasible set by given
prices of resources and a given budget. Many of today’s systems operate in a dynamic
environment. The paper presents approaches for solving dynamic MODNLP problems.
Dynamic MODNLP as an extension of MODNLP can deal with time dependent changes
in systems. The models try to reflect changes in real or simulated time and take into
account that the model components are constantly evolving.
Keywords: De Novo Programming, Dynamic Approach, Multi-Objective Optimization.
167
MO2F (invited)
Dynamic Resampling for Guided Evolutionary
Multi-Objective Optimization of Stochastic Systems
Siegmund, Florian (*)
Deb, Kalyanmoy
Karlsson, Alexander
Ng, Amos
In Multi-objective Optimization many solutions have to be evaluated in order to provide
the decision maker with a diverse Pareto-front. In Simulation-based Optimization the
number of optimization function evaluations is very limited. If preference information
is available however, the available function evaluations can be used more effectively by
guiding the optimization towards interesting, preferred regions. One such algorithm for
guided search is the R-NSGA-II algorithm. It takes reference points provided by the
decision maker and guides the optimization towards areas of the Pareto-front close to
the reference points. In Simulation-based Optimization the modeled systems are often
stochastic and a reliable quality assessment of system configurations by resampling requires many simulation runs. Therefore optimization practitioners make use of dynamic
resampling algorithms that distribute the available function evaluations intelligently on
the solutions to be evaluated. Criteria for sampling allocation can be a.o. objective value
variability, closeness to the Pareto-front indicated by elapsed time, or the dominance relations between different solutions based on distances between objective vectors and their
variability. In our work we combine R-NSGA-II with several resampling algorithms based
on the above mentioned criteria. Due to the preference information R-NSGA-II has fitness
information based on distance to reference points at its disposal. We propose a resampling
strategy that allocates more samples to solutions close to a reference point. Previously,
we proposed extensions of R-NSGA-II that adapt algorithm parameters like population
size, population diversity, or the strength of the Pareto-dominance relation continuously
to optimization problem characteristics. We show how resampling algorithms can be integrated with those extensions. The applicability of the proposed algorithms is shown in
a case study of an industrial production line for car manufacturing.
Keywords: Evolutionary Multi-Objective Optimization, guided search, Reference Point,
Resampling, Simulation-Based Optimization, Stochastic Systems.
168
TU3E (invited)
E-NAUTILUS: A Surrogate based Interactive
Multi-objective Optimisation Method without Trading
Off
Sindhya, Karthik (*) Miettinen, Kaisa
Luque, Mariano
Ruiz, Ana Belén Ruiz, Francisco
Interactive methods are commonly used in multiobjective optimization, where the decision maker (DM) participates in the solution process by iteratively providing preference
information to find her/his preferred Pareto optimal solution(s). Recently, a new interactive multiobjective optimization method called NAUTILUS has been proposed. At each
iteration of NAUTILUS, a new solution is shown to the DM, which dominates the previous one. This avoids the necessity for the DM to trade-off between objectives and only
the last solution is Pareto optimal. We utilize the philosophy of the NAUTILUS method
and propose here a new method called E-NAUTILUS. In the E-NAUTILUS method we
generate a surrogate for the original multiobjective optimization problem using a representation of Pareto optimal solutions, before starting the interactive solution process with
the DM. This enables us to obtain new solutions whenever desired without repeatedly
solving the original multiobjective optimization problem. For this reason, E-NAUTILUS
is especially useful for computationally expensive problems. Any suitable (evolutionary)
multiobjective optimization algorithm is used to generate a set of nondominated solutions
and these solutions are used to construct a surrogate of the original problem. As in the
NAUTILUS method, the solution process is started from the nadir point and at every
iteration a clustering scheme is used to select and show several solutions (which dominate
the solution of the previous iteration) to the DM, who expresses her/his preferences of
how objective function values should be improved by choosing one. Finally, when the DM
has found her/his preferred solution (which is a solution to the surrogate problem), it is
projected onto the actual Pareto optimal front. We illustrate the working principle of the
E-NAUTILUS method using a problem related to electrical industry.
Keywords: Evolutionary Multi-Objective Optimization, Multi-Objective Optimization,
Nadir Point, Preference Information, Reference Point.
169
TH3F (contributed)
Efficient Optimisation of a Vehicle Suspension by
Hybrid Nature Inspiring Method
Syeda, Darakhshan Jabeen (*)
Indian Statistical Institute
In this paper, we propose a new hybrid method based on region reduction division criteria
(RRDC) and advanced real coded genetic algorithm (ARCGA) for solving the suspension
design problem. The problem is concerned with determining the passive suspension parameters of the vehicle for comfortable ride. The two dimensional model of the vehicle
with non-linear suspension springs and linear dampers has been considered. Moreover,
in the dynamical model two passengers in the seated position, one at the front and the
other at the rear position of the vehicle have been considered. To enhance comfort of
the passengers technological constraints as per ISO standards has been introduced to the
optimization problem. The suspension parameters have been found by minimizing the
bouncing transmissibility of the sprung mass subjected to the constraints in time domain.
In order to solve the problem, the constrained optimization problem is first converted into
an unconstrained one by penalty function technique. Then to the transformed problem
the proposed hybrid method is applied. To verify the validity of the obtained parameters,
model simulation has been done over different roads and the results obtained have been
compared with the results obtained with the existing suspension parameters.
Keywords: Artificial Intelligence, Control, Genetic Algorithms, Penalty Methods.
170
MO4C (invited)
Efficient Solutions for Non-Regular Multiobjective
Problems
Hernández Jiménez, Mar´ıa Beatriz (*)
Universidad Pablo de Olavide
Osuna-Gómez, Rafaela
Ruiz-Garzón, Gabriel
Universidad de Cádiz
Necessary optimality conditions for efficient solutions are presented for multiobjective
problems with inequality-type constraints. These conditions are applied when the constraints do not necessarily satisfy any regularity assumptions, and they are based on
the concept of 2-regularity introduced by Izmailov. In general, the necessary optimality
conditions are not sufficient and the efficient solution set is not the same as the KarushKuhn-Tucker points set. So it is necessary to introduce generalized convexity notions. In
the multiobjective nonregular case we give the notion of 2-KKT-pseudoinvex-II problems.
This new concept of generalized convexity is both necessary and sufficient to guarantee
the characterization of all efficient solutions based on the optimality conditions.
Keywords: Non-Regular Problems, Optimality Conditions.
171
WE1B (contributed)
R The AHP Project on the Cloud
Elephas:
Pérez-Rodr´ıguez, Fernando (*) Rojo-Alboreca, Alberto
University of Santiago de Compostela
´
Camino-Saco, Angeles
Gómez-Garc´ıa, Esteban
VSonCloud
Mosquera, Alejandro
In management that we understand how Sustainable should to consider criteria like Economic, Social, Environmental and Technical properly. In an analytical assessment process
which involves multiple criteria and alternatives the decision process is complicated. Besides the criteria may have two types: objective and subjective. The objective criteria are
easily quantifiable, and regardless of the complexity of their evaluation, you can always do
a rigorous statistical analysis or methodologies like optimization by Gauss-Jordan, while
the subjective criteria are associated with uncertainty due to its subjective nature or opinion. In the analysis of subjective criteria must apply methodologies that associate to each
weight or size. These methodologies are collectively referred to as multicriteria analysis,
and although each has its own synthesis calculation, have in common that the decision
was made based on a comparison emitted by a / some decision maker / s and under a
predetermined scale. One of the methodologies used and studied is the Analytic Hierarchy
Process (AHP) proposed by Saaty in the early eighties. This methodology is based on the
allocation of weights to the criteria and / or alternatives through pairwise comparisons,
i.e. a decision maker evaluates their preference between two competing criteria under a
given scale. With the growing popularity of cloud computing, SaaS applications (Software
as a Service) is becoming the software delivery model of choice for organizations around
the world. With SaaS, software is available in the cloud, allowing users to access the
Internet using a thin client such as a web browser. Among the advantages of SaaS can
highlight its rapid deployment, lower maintenance, reduced costs and ease of adding new
features as well as the possibility of changes in real time. In this paper we present Elephas
R an implementation of cloud-based AHP free access for making decisions.
,
Keywords: Analytic Hierarchy Process, Computing Science, Criteria, Decision Support
Systems, Pairwise Comparison.
172
MO4F (contributed)
Elicitation Procedures for the Comparison of
Decisional Maps
Brison, Valerie (*)
Université de Mons
Pirlot, Marc
In the field of land management, it is not uncommon to have a geographic map, representing a region under study, and divided into geographic units. Each unit is assessed
on an ordinal scale describing its degree of suitability for some usage, for instance housing, or its state of degradation with respect to sustainable development criteria. We call
such a map a decisional map. After a while, and for example, after the application of
some policies aiming to improve the situation, the state of the units evolves. For some
units, the state has improved, and for some others, it has deteriorated. What we want
to know is whether the global state of the map has improved or not. The purpose of
this work is to provide models to help a decision maker to compare the state of a region
at different stages of its evolution. We formulate two mathematical models and we provide an interactive elicitation procedure to determine all the parameters of the models
developed. For this purpose, we transpose a widely used method in the theory of decision under risk, namely the comparison of lotteries. We interpret lotteries as maps and
we formulate questions to the decision maker in terms of comparisons of well-chosen maps.
Keywords: Elicitation, Map Comparison, Preferences.
173
TU5G (invited)
Engineering Multiobjective Shortest Path Heuristics
Zaroliagis, Christos (*)
CTI & University of Patras
Multiobjective shortest path is a core problem within multiobjective optimization, appearing in applications such as QoS routing in communication networks, traffic equilibria,
transport optimization as well as route and itinerary planning. It is an extension of the
single criterion shortest path problem using a cost vector (instead of a scalar) per network edge, representing multiple (often conflicting) objectives or criteria. Even though
numerous efficient algorithms exist for the single criterion shortest path problem, the
multiobjective counterpart of the problem is much harder (NP-hard), since the optimal
solution (captured by the well-known concept of the Pareto set) is typically exponential
in size. Two main approaches are followed in order to reduce the computation effort for
solving the multiobjective shortest path problem. The first one uses approximation and
computes optimal solutions up to a certain factor. Approximation techniques do not necessarily yield exact solutions, but are adequately fast to be used in practice. The second
approach uses heuristic improvements to speed-up existing algorithms. Such techniques
yield exact solutions, but it is considerably more difficult to achieve good performance.
In this work we focus on the latter approach, motivated by the great demand in practical
applications to achieve efficient and exact multiobjective shortest paths. Building upon
a new graph structure specifically suited for large-scale networks, we present new implementations of heuristic algorithms (including NAMOA*, the currently best such heuristic)
for the solution of the multiobjective shortest path problem. We enhance the heuristics
with further optimizations and experimentally evaluate the performance of our enhanced
implementation on real world road networks achieving 10 times better performance with
respect to the best previous study.
Keywords: A* Search, Heuristic Search, Multi-Objective Shortest Path Problem.
174
TU5G (invited)
Engineering Parallel Bi-Criteria Shortest Path Search
Erb, Stephan (*)
Karlsruhe Institute of Technology
Sanders and Mandow (IPDPS 2013) present a parallel label-setting algorithm for the
multi-criteria shortest path problem. The algorithm is a generalization of Dijkstra’s
single-criteria shortest path algorithm for the multi-criteria case. Using a Pareto queue,
a multi-dimensional generalization of a priority queue, the algorithm is able to parallelize
all additional work when going from one to multiple criteria. For the bi-criteria case it can
even be shown that the algorithm performs less work than previous sequential algorithms.
We discuss in how far these theoretical results translate into efficient implementations for
modern shared memory multiprocessors. In particular, we focus on a cache-efficient implementation of the bi-criteria case and present an extensive evaluation of the algorithm
and its underlying data structures.
Keywords: Multi-Objective Optimization, Parallel Computing, Performance Measurement, Shortest Path.
175
MO4G (contributed)
Enhancing Portfolio Risk Analysis - Balancing the
Past, the Future, the Objective, the Subjective
Myers, Chris (*)
Risk analysis is an important element of the decision making process of insurance companies. Market participants such as capital providers, rating agencies and regulators
recognize this importance, and as a result often want transparency from insurers as to
their risk profiles. This transparency may include reporting on extreme risk measures such
as value-at-risk or tail value-at-risk with respect to investment portfolios. Most methods
rely heavily, if not exclusively, on historical risk and return data to calculate and forecast
loss impacts given stress environments. However, historical risk and return characteristics are far from a perfect or reliable indicator of the future, particularly under extreme
environments. Moreover, blindly following any quantitative modeling framework without
subjective overlays can make for misleading conclusions regarding the risk profiles that
these frameworks were meant to articulate. This discussion will show examples of the
potential limitations of risk analysis that is purely quantitative in nature, and will introduce an approach that blends historical and forecasted risk and return assumptions for
improved risk analysis and decision making. For example, one can use historical information to understand certain useful characteristics that define the shape of the distribution
of returns for invested assets. This understanding could be enhanced by overlaying current
and prospective views of those returns, perhaps supported by forward looking economic
indicators, which would define the body of that same distribution. Thus one has an analytical approach that balances objective and subjective decision analysis. The focus of the
discussion will be towards investment portfolios typical of non-life insurance companies,
but may very well be appropriate for other investment strategies.
Keywords: Decision Support, Finance, Investment Analysis, Risk Analysis.
176
TU4B (contributed)
Enhancing Smart Mobile Devices by the Fuzzy
DEMATEL based Network Process
Tzeng, Gwo-Hshiung (*)
Kainan University
Huang, Chi-Yo Kao, Yu-Sheng
National Taiwan Normal University
The smart mobile device market surged dramatically as the embedded system and wireless
communication technology advances during the past decade. As a result, people depend
heavily on smart mobile devices for retrieving information ubiquitously. Therefore, efficient computation capability is essential for smart mobile devices. In order to explore
the factors influencing customers’ acceptances of smart mobile communication technologies and enhance the less efficient features accordingly, a fuzzy multiple criteria decision
making (FMCDM) framework will be defined. The fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) based Network Process (DNP) will be used to construct
the casual relationship and derive weights being associated with the factors. The factors
with higher weights are the more influential ones. Meanwhile, the performance scores
and the satisfactory level corresponding to the factors as well as the scores of competing smart mobile devices targeting at the same market segmentation will be measured
based on performance measurement APPs as well as experts’ opinions. The factors with
lower performance scores or satisfactory level will be enhanced by appropriate innovation
strategies being selected based on the Grey Relational Analysis. An empirical study on
an Android based smart mobile device being provided by a world leading Taiwanese IT
vendor will be used to verify the feasibility of the proposed framework. The analytic
results being derived by using the proposed Fuzzy DNP based framework methods can
be the basis for IT managers and marketers’ strategy definitions.
Keywords: DEMATEL-Based ANP, Fuzzy MCDM, Innovation Strategy, New Product Development, Smart Devices.
177
TU2E (invited)
Entropy Measures to Control Robustness in Ordinal
Regression Models
Greco, Salvatore (*)
Siskos, Yannis
University of Piraeus
Slowinski, Roman
Poznan University of Technology
Within Multiple Criteria Decision Aiding (MCDA), ordinal regression methods build a
preference model in the form of a value function or an outranking relation on the basis of
some preference information provided by the Decision Maker (DM). Since very often there
exists a plurality of instances that represent equally well the preference information given
by the DM, Robust Ordinal Regression (ROR) has been proposed with the aim of considering the whole set of compatible instances of a chosen preference model representing the
preference information given by the DM. ROR provides a recommendation of the MCDA
procedure in terms of possible preference, in case the preference holds for at least one compatible instance, and necessary preference, in case the preference holds for all compatible
instances. Using ROR there is a risk that the necessary and possible preferences are too
vague because the set of compatible instances is “too large”. In order to control this
aspect of the ROR methodology, we propose to measure the set of compatible instances
of the considered preference as the hypervolume of the set of corresponding preferential
parameters. Moreover, we propose to build a measure of the uncertainty due to ambiguous representation of DM’s preferences based on a credibility of preference obtained from
application of Stochastic Multiobjective Acceptability Analysis (SMAA) methodology to
ROR. The certainty about the truth or falsity of the preferences can be modeled through
this credibility of preferences using the concept of Shannon entropy. Entropy measures
as well as the hypervolume of the sets of preference parameters compatible with DM’s
preference information can be used to assist the process of elicitation of preferences by
the DM, in order to evaluate the tradeoff between the decreasing confidence of the preference information given by the DM and the reduction of the hypervolume of compatible
preference parameter set and the improvement of the entropy measures.
Keywords: Entropy, Multi-Criteria Decision Aid, Ordinal Regression, Robust Ordinal
Regression, Stochastic Multi-Objective Acceptability.
178
MO2E (contributed)
Error Correction Model in Classification by Using
Multiple-Criteria and Multiple-Constraint Levels
Linear Programming
Wang, Bo (*) Xianhua, Wei
University of Chinese Academy Sciences
Shi, Yong
UESTC
Nowadays, optimization based classification methods are widely researched, support vector machine (SVM) for example. In addition to this, based on goal programming (GP) and
multiple-criteria linear programming (MCLP), various classification methods according to
linear system are posed and developed. And then, considering multiple-constraint levels,
some researchers proposed algorithms by using multiple-criteria and multiple-constraint
levels linear programming (MC2LP). Until now, a system of linear models, MCLP and
MC2LP, has been applied to classification problems. However, these previous methods
cannot help increasing the accuracy of classification directly. That is to say, there is no
method focuses on minimizing the misclassified cases, which is a key issue in evaluating
of a certain classification algorithm. Furthermore, in Statistics, misclassified cases can be
seen as two types of error. At the same time, MCLP model finds a solution to project all
the points into a one dimension space so that the value of cutoff can discriminate which
group every point belongs. According to this fact, the correction of two types of error
can be obtained through changing the cutoff of the classification hyperplane. Therefore,
we expect to make use of MC2LP to get a changeable cutoff, which has a special connection with the right hand side of the constraints. In this situation, a brand new MC2LP
based model, which can be applied to deal with two types of error respectively, can be
developed. In this paper, we pose a new MC2LP model that can correct two types of
error. Especially, it can be developed into a pair of models, which can deal with two types
of error respectively. Besides, we will give the matrix representation of the new models
and discuss some properties of them. At last, some experiment results based on the new
models will be displayed to show the new model’s capability in correcting two types of
error.
Keywords: Matrix Representation, Multi-Constraint Levels, Multi-Objective Linear
Programming, Multi-Objective Optimization, Two Types Of Error.
179
WE2G (invited)
Estimating the Pareto Front of a Hard Bi-Criterion
Competitive Location Problem
Lancinskas, Algirdas (*)
Vilnius University
Fernández Hernández, Pascual
Universidad de Murcia
Pelegr´ın, Blas
Zilinskas, Julius
Vilnius University
We dealt with a location problem for an expanding franchise type firm in competition
with other firms in a geographical area. The aim of the firm is maximizing the market
share captured by the new facilities and minimizing the the lost of market share of the
old facilities caused by the entering of the new facilities in the market. The market share
of each facility is estimated assuming that customers are served by the most attractive
facility. We introduce a new tie breaking rule to serve the customers for which there are
more than one facility with maximum attraction which lead to solve a bi-objective hard
nonlinear optimization problem. An heuristic algorithm is proposed which let obtain a
good approximation of the Pareto front when the firm have to select a fixed number of
new facilities in a finite set of candidates.
Keywords: Evolutionary Multi-Objective Optimization, Location.
180
TH2C (contributed)
Evaluating Clinical Performance of Pediatric
Emergency Physicians with a Help of Data
Envelopment Analysis Model
Michalowski, Wojtek (*) Javier, Fiallos
University of Ottawa
Patrick, Jonatha
Farion, Ken
Children’s Hospital of Eastern Ontario
The clinical performance of physicians working at a Pediatric Emergency Department
(PED) is a key factor in determining the ability of PED to provide efficient and quality
patient care. The assessment of PED physicians is a multiple criteria task involving such
measures as the patient health outcomes, the timeliness of care, the throughput of patients, and the efficient use of resources, however most of the literature concerned with
physician performance evaluation uses a single criterion such as medical competency or
quality of care. There were few attempts at multi-criteria evaluation of physicians’ performance and all of them applied some form of Data Envelopment Analysis (DEA). None
of these evaluations consider criteria related to the quality of health outcomes and the
timeliness of care. The DEA model proposed here focuses on the clinical performance
of PED physicians. This performance is assessed using a criteria related to diagnostic
effectiveness, timeliness of care, throughput of patients by individual PED physicians and
quality of health outcomes measured for these patients. These four criteria capture the
multi-faceted nature of the PED physicians’ clinical work and, to the best of our knowledge, it is the first time when such a comprehensive evaluation of clinical performance has
been proposed. A DEA model known as the Slacks Based Measure model was selected as
the evaluation method. This model allows measuring the excess of inputs and shortfall
of outputs that characterize an inefficient PED physician’s performance. It calculates
a score that captures these inefficiencies, identifying benchmark physicians. While use
of the model assumes that the physicians have control over both their inputs and outputs, model’s solution supports the development of improvement plans for each inefficient
physician. The model was developed using data reflecting operations of PED in one of
Ottawa (Canada) teaching hospitals.
Keywords: Clinical Performance, Data Envelopment Analysis, Healthcare, Multi-Criteria
Decision Making.
181
MO3E (contributed)
Evidential Reasoning Rule for Criteria Combination in
MCDM
Xu, Ling (*)
Yang, Jian-Bo
A unique Evidential Reasoning (ER) rule is established for combining multiple criteria
with various weights and reliabilities in decision and judgement making. The novel concept of Weighted Belief Distribution (WBD) is proposed and extended to WBD with
Reliability (WBDR) to complement Belief Distribution (BD) as introduced in DempsterShafer (D-S) theory of evidence. The implementation of the orthogonal sum operation
on WBDs (WBDRs) leads to the establishment of the new ER rule, which constitutes a
generic conjunctive probabilistic reasoning process, or a Bayesian inference process when
used to combine probability information. It is shown that the ER combination algorithm
developed for multiple criteria decision analysis under hybrid uncertainty is a special case
of the ER rule when the reliability of information is reflected by its weight and the weights
of all criteria are normalised. It is proven that Dempster’s rule is also a special case of
the ER rule when each piece of information is fully reliable, with the latter completing
and enhancing the former by identifying how to combine multiple pieces of fully reliable information that are highly or completely conflicting through a so-called reliability
perturbation analysis. The main properties of the ER rule are explored to facilitate its
application. Some typical existing rules are discussed and compared with the ER rule.
Numerical and simulation studies are conducted to show the validity and features of the
ER rule.
Keywords: Bayesian Inference, Belief Distribution, Dempster-Shafer Theory, Evidential Reasoning, Multi-Criteria Decision Analysis.
182
FR2G (contributed)
Experimental Evaluation of Polynomial and Copula
Functions for Qualitative Option Ranking
Bohanec, Marko (*)
Joˇzef Stefan Institute
Mileva-Boshkoska, Biljana
We address the problematics of full option ranking in MCDA. We start from qualitative
models created according to DEX methodology, which ensures option classification into
preferentially ordered classes. To obtain option ranking within each of the classes, the
QQ method is used as an extension to DEX. QQ constructs a family of linear regression
functions, each one corresponding to a different class. The main drawback of QQ is that
it frequently leads to partial ranking or fails to satisfy the monotonicity of options within
classes. Hence we modify QQ to investigate the usage of polynomial and copula-based
regression functions for option ranking. In order to find the best polynomial function for
a given set of options we use the CIPER algorithm. CIPER uses a specific heuristics to
define and search the space of possible polynomial functions for the given set of options.
As a results it finds one or several polynomial functions that satisfy the heuristics and
that provide best fit for the data. Copulas are functions that capture the non-linear dependences among random variables. To use copulas for option ranking, each attribute is
considered as a random variable. The variables are nested into hierarchical bi-variate copula structures to determine the non-linear dependences among all attributes at hand. The
hierarchical copula structures are used to obtain regression functions for option ranking.
In this paper we empirically evaluate the proposed methods on three groups of experiments with two, three and four input attributes and a class attribute. We show that
polynomial functions manage to provide best results of fulfilling the monotonicity in the
first two experiments, however they lead to partial ranking. Finally, copulas manage to
provide best results in terms of satisfying both the monotonicity and full option ranking
in all three groups of experiments.
Keywords: Copula-Based Model, Linear Model, Multi-Criteria Ranking, Polynomial
Model, Qualitative Multi-Criteria Model.
183
WE1F (invited)
Explicit Equations for Non-Dominated Frontier in a
Portfolio Selection Problem based on Soft Computing
Salda˜
na, Alberto Carlos (*) Cadenas, José Manuel
Garrido, Maria del Carmen Martinez, Raquel
University of Murcia
The more natural approach to the portfolio selection problem has the double objective of
maximizing the return on an investment while minimizing risk. The set of risk-return pairs
of all the efficient portfolios is called non-dominated frontier, and so the decision-support
techniques designed to assist an investor in these problems consist of computing and analyzing this frontier. We address two kinds the constraints which make the corresponding
model more involved from a computational viewpoint: semicontinuous variable constraints
and cardinality constraints. The non-dominated frontiers of such problems become more
irregular and new specific computation techniques are required. We describe a multiobjective GA that allows us to obtain the non-dominated frontier of the proposed portfolio
problem through explicit expressions of the arcs of parabola. ENCODING: Individuals
are binary strings. In addition, each individual has associated seven values: (a,b,c) which
describe the parabola and intervals [rmin,rmax], [Rmin,Rmax] in which the parabola is
defined. FITNESS: We define the fitness as the relative Level of Non-Dominance (LND):
an individual ’y’ has a LND value obtained from an interval [A,B] into [Rymin,Rymax]
where its parabola (a,b,c) dominates to any other parabola corresponding to other individual ’y1’. SELECTION AND GENETIC OPERATORS: a) We incorporate elitism.
The best individuals are those with a fitness value of LND=1. b) The selection is by
means of binary tournament and due to the structure of individual, we define a special
binary tournament from the fitness function and the distance of niches. c) Due to the
individual structure we define an hybrid crossover. d) The used mutation is the classical
mutation of a gene. The output is the set of individuals with LND other than 0. These
individuals describe the non-dominated frontier through explicit expressions of the arcs
of parabola. From these expressions all values of portfolios are obtained.
Keywords: Multi-Objective Genetic Algorithms, Non-Dominated Frontier, Portfolio Selection, Soft-Computing.
184
MO3G (invited)
Facing the Energy Transition: An Illustration how
Value-Focused Thinking helps to Find the Right
Answers
Krampf, Peter (*)
Siebert, Johannes
Germany is facing a huge disruptive change in its energy system. The speed of the transition from the fossil fuel and nuclear energy age to the renewable energies had been
tremendously accelerated after Fukushima and the government´s decision to phase out of
nuclear energy in the next 10 years. To ensure long-term competitiveness, to define the
future strategy and to agree on the right implementation steps, currently traditional German utilities face decisions with huge consequences. In the following 20 years decentralized
renewable energy solutions, cost intensive offshore wind parks and the decommissioning
of centralized conventional power plants have to be planned and realized. Like in other
industries, there is the tendency to deal with decision problems isolated from each other
without focusing on the big picture. Thereby it is often only vague defined what and how
should be achieved. Furthermore the search of alternatives is based on traditional thinking, which is highly dangerous in a disruptive change. Value-focused Thinking provides
methods to identify and structure objectives (what should be achieved). The objectives
can be used to create systematically alternatives and decision opportunities (how this
could be achieved) which the company was not aware of before. Even known in literature, so far there had been no practical use in the energy sector. In this presentation
we demonstrate based on a practical case example how Value-focused Thinking is able
to support this challenge one of the top 12 utilities in Europe, located in Germany in
linking future decisions explicitly to their objectives. After demonstrating the challenge,
we illustrate the identification and structuring of objectives. We show how the objective‘s
networks is affected by the energy transition from the fossil fuel and nuclear energy age
to the renewable energies. Finally we demonstrate how alternatives could be created in a
specific decision problem under consideration of its objectives.
Keywords: Case Example, Energy System, Utility, Value Focus Thinking.
185
FR2D (contributed)
Factors Affecting Marketing Channel Choice Decisions
in Citrus Supply Chain: A Conceptual Framework
Siddique, Muhammad Imran (*)
Massey University
Garnevska, Elena
Massey University
Marr, Norman
Massey University
Citrus fruit supply chain is multifaceted and complex due to the presence of a large number of intermediaries. Selection of an appropriate marketing channel by growers involves
a complex decision making process. A number of scocio-economic, demographic and psychological factors are involved in this decision making process. This paper reviews the
existing factors affecting decision making in agribusiness and models and methods employ
to measure and analyse these factors. A conceptual framework based on multi-attribute
utility theory is proposed to measure the most important factors affecting the citrus
growers and pre-harvest contractors marketing channel choice decisions in Pakistan. A
multivariate decision analysis technique, conjoint analysis, is proposed to identify and
evaluate the major factors affecting marketing channel choice decisions of citrus growers and pre-harvest contractors. The research would provide valuable information about
citrus growers and pre-harvest contractors decision making process and thus would contribute to improving the efficiency and effectiveness of citrus industry.
Keywords: Conjoint Analysis, Decision Making, Multi-Attribute Utility Theory, MultiVariate Decision Analysis.
186
TH3C (contributed)
Feature Selection for Classification Using
Multi-Objective Optimization
Cimen, Emre (*)
Anadolu University
Ozturk, Gurkan
Anadolu University
Feature extraction is necessary and the key point for classification problems. Especially
in real time systems, like object detection systems, features must be extracted from the
image rapidly. There is a trade of computation time with accuracy in classification. It
is not possible to exctract all features because of time constraint. Also using less features decrease classification accuracy. In this study we use a mathematical programming
approach to find classification function with promising features by considering two objectives: number of features and classification accuracy.
Keywords: Classification, Feature Selection, Mathematical programming.
187
MO2F (invited)
FEMOEA: a Fast and Efficient Multi-Objective
Evolutionary Algorithm
López-Redondo, Juana (*)
University of Granada
Fernández-Hernández, José
Mart´ınez-Ortigosa, Pilar
University of Almer´ıa
A multi-objective evolutionary algorithm which can be applied to many nonlinear multiobjective optimization problems is proposed. Its aim is to quickly obtain a fixed size
set of points approximating the complete Pareto-front. It adapts ideas from different
multi-objective optimization evolutionary algorithms, but also incorporates new devices.
In particular, the search in the space is carried out on promising areas determined by
hyperspheres (subpopulations). Their radii are not fixed; on the contrary, they decrease
as the optimization procedure evolves. This mechanism of a decreasing radius helps to
maintain a balance between exploration and exploitation of the search space. Additionally, a new local method which accelerates the convergence of the population towards the
optimal Pareto-front, has been incorporated. Basically, this method is an extension of the
local optimizer SASS to multi-objective problems, which improves a given solution along
a search direction (no gradient information is used). Finally, a termination criteria has
been also incorporated, which stops the algorithm whenever the changes experimented in
the candidate Pareto-front are negligible (less that a given tolerance) during three consecutive iterations. To know how far two approximation sets are from each other, a new
Hausdorff-like distance is proposed. In order to analyze the algorithm performance, it has
been compared to reference algorithms NSGA-II and SPEA2 on a set of twenty instances,
which includes, among others, the ZDT and DTLZ suites of benchmark problems. Several
quality indicators have been considered, namely, hypervolume, average distance, additive
epsilon indicator, spread and spacing. According to the computational results and statistical analysis performed, the new algorithm, named FEMOEA, outperforms, on average,
both NSGA-II and SPEA2 in all the quality indicators.
Keywords: Improving Method, Non-Linear Multi-Objective Optimization, Quality Indicators, Stopping Rule.
188
FR3C (contributed)
Finding Nadir Points in Multi-objective Integer
Programs
Lokman, Banu (*)
Aalto University
Koksalan, Murat
The nadir value of a criterion is the worst possible value in that criterion over the set
of nondominated points. Nadir points have important uses in multiple criteria decision
making problems. Knowing the nadir point and the ideal point allows us to properly
scale the problem. The nadir point is also a good starting point for algorithms that start
from dominated points and try to converge to preferred points. Finding the nadir point
is not straightforward when there are three or more criteria. It is particularly hard to
find the nadir point for Multi-objective Integer Programs (MOIPs). We develop an exact
algorithm that finds the nadir point for any MOIP. In order to find the nadir value of
a given criterion in a maximization problem, we define an upper bound for the nadir
in that criterion. We also set lower bounds to each criterion by using the properties of
the nadir point. We then iteratively generate points in a region defined by these bounds
and update the bounds using these points. At each iteration, the algorithm tries to find
a nondominated point having a smaller value than the upper bound in the criterion of
interest. The algorithm stops when no nondominated point can be found in the searched
region. We also develop an approximation algorithm that finds lower and upper bounds
for the nadir value in each criterion with a desired level of accuracy. The approximation
algorithm stops when the relative gap between the bounds falls below a desired value. We
test the performance of the algorithms on several instances of multi-objective assignment,
knapsack, and shortest path problems. Our computational experiments on three and four
criteria problems show that the algorithms work well.
Keywords: Integer Programming, Multi-Objective Optimization, Nadir Point.
189
TH2G (invited)
Finding Representative Subsets in Multiobjective
Discrete Optimization
Paquete, Luis (*)
Ponte, An´ıbal
Polytechnic Institute of Set´
ubal
Stiglmayr, Michael
Vaz, Daniel
We consider several formulations of the problem of selecting a representative subset of
alternatives from the nondominated set of a biobjective discrete optimization problem,
according to some property of interest. These formulations can be seen as a special type
of facility-location problems. In particular, we introduce algorithms to find subsets with
respect to the epsilon-indicator, coverage, uniformity and combinations thereof. We analyse these algorithms in terms of time and space complexity and discuss their performance
in an experimental setting. Extensions to more objectives are also presented.
Keywords: Combinatorial Optimization, Multi-Criteria Combinatorial Optimization,
190
TH2G (invited)
Finite Representation of Non-Dominated Sets in
Multi-Objective Linear Programming
Ehrgott, Matthias (*)
Shao, Lizhen
University of Science and Technology Beijing
We address the problem of representing the continuous and non-convex set of nondominated points of a multi-objective linear programme by a finite subset of such points.
We prove that a related decision problem is NP-hard. Then we illustrate the drawbacks of
the known global shooting, normal boundary intersection, and normal constraint methods
concerning the coverage of the non-dominated set and uniformity of the representation
by means of examples. We propose an algorithm which combines the positive elements of
both the global shooting and normal boundary intersection methods but overcomes their
limitations of. We prove that the new algorithm computes a set of evenly distributed
non-dominated points for which the the coverage error and the uniformity level can be
measured. Finally, we illustrate the usefulness of the new algorithm with an application
in the planning of radiation therapy for cancer treatment and present numerical results
for randomly generated multi-objective linear programmes.
Keywords: Linear Programming, Multi-Objective Optimization, Representative Set.
191
MO3F (contributed)
Framing Mechanical Design as Multiple Criteria
Decision Making Processes
Kaliszewski, Ignacy (*)
Systems Research Institute
Kiczkowiak, Tomasz
Technical University of Koszalin
Miroforidis, Janusz
Treeffect Co, and Systems Research Institute
In the presentation we pursuit our earlier development [1] on coupling local evolutionary computation based search for Pareto front elements with a preference capturing and
passing mechanism. This time we approach two mechanical engineering design problems,
namely the problems of round tube beam and pneumatic actuator design. We use those
two problems, analysed and solved before by classical structural mechanics and optimisation methods, to illustrate the versatility of the development and its natural scalability,
mainly trough the use of evolutionary computations, to medium and large scale practical
problems. The development offers itself as a reasonable option to all the approaches to
MCDM which presume the knowledge of Pareto fronts, or at least the knowledge of fair
representations of those sets. We use also those two problems to illustrate an interpretation of the preference capturing and passing mechanism we use as a universal communication interface - a universal language of sort - between humans and computer based
multiple criteria optimisation models. We support our views by examples from mechanical
engineering literature where needs for such a language/interface to cope with interdisciplinary design challenges have been articulated for long. [1] Kaliszewski I., J. Miroforidis,
D. Podkopaev, Interactive Multiple Criteria Decision Making based on preference driven
Evolutionary Multiobjective Optimization with controllable accuracy. European Journal
of Operational Research, 216, 2012, 188-199.
Keywords: Evolutionary Multi-Objective Optimization, Mechanical Engineering, MultiCriteria Decision Making, Multi-Objective Optimization.
192
WE1F (invited)
Fuzzy Decentralized Production and Distribution
Planning in an Uncertain Environment
Diaz-Madro˜
nero, Manuel (*)
CIGIP-UPV
Mula, Josefa
Peidro, David
A supply chain (SC) may be considered an integrated process in which a group of several organizations, such as suppliers, producers, distributors and retailers, work together
to acquire raw materials with a view to converting them into end products which they
distribute to retailers. One way to coordinate operations in this context is by centralized
planning, in which a single decision maker has access to all SC members information
details (i.e. stocks, production capacities, costs, etc.) in order to create optimal global
plans for all partners in the SC. However, although it has been reported that the benefit
of information sharing is significant in reducing total SC costs and optimizing capacities, materials and production decisions, SC partners could be reluctant to reveal all of
their information trying to locally optimise their own objectives, generating unsuitable
or infeasible centralized plans. On the other hand, the competitive and globalized environment and prominent role of customers’ preferences could imply an important degree
of uncertainty in production and distribution decisions. It can be distinguished between
randomness, or epistemic uncertainty or lack of knowledge of the parameter values. In
order to overcome the need for confidential information of all members in SC by a single decision maker and to generate a profitable production and distribution plan under
uncertainty conditions we propose a modified approach of the decentralized coordination
scheme by Jung and Jeong (2005) with minimal information sharing by incorporating the
ranking fuzzy numbers method proposed by Jiménez (1996) to handle the lack of knowledge in model parameters. This work has been funded by the Universitat Politècnica
de València project: ’Quantitative Models for the Design of Socially Responsible Supply
Chains under Uncertainty Conditions. Application of Solution Strategies based on Hybrid
Metaheuristics’ (PAID-06-12).
Keywords: Collaborative Planning, Distributed Decision Making, Fuzzy Sets, Supply
Chain Management, Uncertainty Modelling.
193
TU2D (invited)
Fuzzy Linguistic Multi-Criteria Morphological
Analysis applied to Scenario Planning
Cruz Corona, Carlos (*)
Lamata, Mar´ıa T.
Masegosa, Antonio D.
Villacorta, Pablo
Nowadays, companies that compete for a place in rapidly evolving markets of modern society know the necessity of good practices that help them to deal with uncertainty, make
better decisions in the present -especially regarding management of innovation- and hence
anticipate the future. The most employed technique is scenario planning, which consists
of a set of tools that emphasize the systematization of creative thinking to uncover possible future scenarios. They consider possibilities that may have escaped to ad-hoc human
studies by procedures which capture the most important aspects of both current situation
and feasible futures, highlight implications and impact of present decisions, and sketch the
steps to go from the present to the desired future. Some of the methodologies proposed
to develop this general idea are Cross Impact Analysis, Trend Impact Analysis, Fuzzy
Cognitive Maps and Morphological Analysis (MA). We focus on MA, which is aimed at
identifying all scenarios that result from the combination of non-quantifiable states that
the system variables can take, as well as the feasibility (compatibility) of each combination. An interesting variant of MA is MORPHOL, which also allows evaluating the
probability of each scenario by aggregating the probabilities of each of the states adopted
by the variables in that scenario. The probabilities mentioned above are numerical values
provided by experts based on their own expertise and intuition, so they are subject to
certain vagueness not captured by a crisp value. Furthermore, scenarios are judged only
in terms of probability but no other criteria are considered. Therefore it is impossible
to uncover those scenarios that are the most desirable, catastrophic or feasible, or even
the best ones when considering all criteria simultaneously. In this preliminary study, we
try to overcome both issues by a novel MCDM procedure of MA that introduces multiple
criteria and linguistic fuzzy labels scenario evaluation.
Keywords: Applications of MCDM, Computing With Words, Fuzzy Numbers, Linguistic
Variables, Morphological Analysis, Scenario Planning, Scenarios, Soft-Computing.
194
TU2D (invited)
Fuzzy Meta-Goals in Goal Programming
Jiménez López, Mariano (*)
Universidad del Pa´ıs Vasco
Arenas-Parra, Mar
Bilbao, Amelia
In goal programming it is not uncommon that the DM does not accept the first solution.
In order to address this problem, additional meta-goals, derived from the original set of
goals, have been proposed in the literature. The most common meta-goals are related to
the percentage sum of unwanted deviations, the maximum percentage deviation and the
percentage of achieved goals. In this paper we propose relaxing the last ones by referring
to “nearly achieved” goals. This imprecise expression is modeled by a fuzzy set whose
membership function represents the degree in which the target is nearly achieved. In
the same way the second meta-goals, related to maximum percentage deviations, can be
graduated by fuzzy sets. This way we obtain a model simpler than those obtained with
crisp meta-goals, whose solution, in addition, could be better balanced. Some illustrative
examples are solved.
Keywords: Fuzzy Sets, Goal Programming, Meta-Goal.
195
WE1F (invited)
Fuzzy Multi-Criteria Decision Models for the Portfolio
Selection Problem
Vercher, Enriqueta (*)
Berm´
udez, José D.
The portfolio selection problem deals with the decision about which assets a portfolio
should comprise considering a suitable tradeoff between return and risk. Regarding the
quantification of the uncertainty of future returns on a given portfolio we use LR-type
fuzzy variables whose expected moments measure the risk and profitability of the investment. We formulate a fuzzy mean-absolute deviation model where investor preferences,
portfolio size and diversification conditions are introduced by means of constraints. We
solve the corresponding multi-objective optimization problem within the multi-criteria
framework by using an evolutionary procedure which provides non-dominated portfolios
in the approximated Pareto frontier. To select the best portfolio among the Pareto solutions we apply a ranking strategy based on the concept of fuzzy Value-at-Risk. The
performance of this Soft-Computing approach is analyzed using a set of assets from the
Spanish stock market.
Keywords: Evolutionary Multi-Objective Optimization, LR-Type Fuzzy Variables, MultiObjective Optimization, Portfolio Selection, Uncertainty Modelling.
196
FR3F (contributed)
Fuzzy VIKOR Approach for Assessing the
Vulnerability of the Water Supply to Climate Change
and Variability in South Korea
Chung, Eun-Sung (*)
Seoul Nat University of Science & Tech
Lee, Boram
Won, Kwang-Jae
This study assesses the vulnerability of the water supply to climate change and variability
in the South Korean provinces for the present and future with a modified fuzzy VIKOR
approach. Adaptive capacity and climate exposure, the key indicators of vulnerability, are
profiled with the Delphi surveys based on the vulnerability concept, in which vulnerability is defined as a function of sensitivity. The fuzzy VIKOR method is used to aggregate
the key indicators into a vulnerability score because it provides a compromise solution
considering overall satisfaction and regret of the selection of wrong provinces. Markedly
different rankings between the fuzzy VIKOR method and a conventional weighted sum
method suggest the importance of considering regret of the selection of wrong provinces
and including the uncertainty of information in the vulnerability assessment and consequent decision making for adaptation. Furthermore, diverse vulnerability rankings with
the six different future scenarios suggest the need for robust decision making given the
uncertainty of such diverse scenarios.
Keywords: Fuzzy Sets, Fuzzy VIKOR, TOPSIS, Water Resources Management.
197
MO3G (invited)
Generating an Optimal Set of Alternatives using
Siebert, Johannes (*)
Broadness and quality of identified alternatives are the basis of sustainable decisions.
Bond et al. (2008) found out that decision makers are not aware of their objectives. We
have conducted an empirical study which test setup was a straightforward extension of
Bond et al. (2008) to analyze whether decision makers are aware of their alternatives. Our
study confirms the hypothesis that decision makers are not aware of their alternatives, too.
According to Keeney (1992), Value-focused Thinking enhances broadness and quality of
alternatives identified by decision makers. We conducted a second empirical study which
measured broadness and quality of alternatives identified by participants under different
conditions. The results confirm the hypothesis that stimulation due to thinking about
ones objectives leads to more and better alternatives. We also analyze the hypothesis
that means are especially fruitful grounds to create alternatives. We have tested the
impacts of different stimuli on the number, broadness and measurements of quality of the
alternatives created. The analyzed stimuli are the use of means objectives, fundamental
objectives, a master list of objectives, and a means-network.
Keeney, R. L. (1992). Value-Focused Thinking a Path to Creative Decisionmaking. Bond,
S. D., Carlson, K. A., & Keeney, R. L. (2008). Generating Objectives: Can Decision Makers Articulate What They Want? Management Science Vol. 54, No. 1, pp. 56-70.
Keywords: Alternatives, Empirical Study, Objectives, Value Focus Thinking.
198
FR2E (invited)
Generation of the Choquet Optimal Set of
Multiobjective Combinatorial Optimization Problems
Lust, Thibaut (*)
LIP6 - CNRS
Rolland, Antoine
Université Lyon 2
We study the generation of Choquet optimal solutions of multiobjective combinatorial
optimization problems. Choquet optimal solutions are solutions that optimize a Choquet
integral. The Choquet integral is used as an aggregation function, presenting different
parameters, and allowing to take into account the interactions between the objectives.
We develop a new property that characterizes the Choquet optimal solutions. From this
property, a general method to easily generate these solutions is defined. We apply the
method to the multiobjective knapsack problem. We show that Choquet optimal solutions that are not weighted sum optimal solutions represent only a small proportion of
the Choquet optimal solutions and are located in a specific area of the objective space,
but are much harder to compute than weighted sum optimal solutions.
Keywords: Choquet Integral, Combinatorial Optimization, Multi-Objective Optimization.
199
MO2E (contributed)
Goal Programming Approach to Workforce Scheduling
Problem for a Restaurant
C
¸ o¨l, G¨
ul¸cin (*)
Osmangazi University
Hasg¨
ul, Servet
Osmangazi University
Workforce is one of the most important source of an enterprise and effective use of it is
an important issue. Creating a schedule that is compatible with the employee skills and
preferences increases quality of the services. In this study employees have preferences on
their days- off and work shifts. In addition, each employee has different skills. Using goal
programming approach employees are assigned to daily shifts, days- off and tasks as much
as possible to suitable to their preferences and skills. In addition gaps between the shifts
and deviations from maximum and minimum working hours an employee must work during the scheduling period are minimized. The proposed model is applied in a restaurant
for 1 and 2 months scheduling periods with heterogenous workload using prioritized goal
programming approach. The results are compared with the schedule that is created by
team leader. As a result effective schedules are obtained using goal programming method.
Keywords: Goal Programming, Preferences, Workforce Scheduling.
200
MO2B (contributed)
Goal Programming Model for Management
Accounting: A New Typology
McGillis, Sheila (*)
Aouni, Belaid
The environment within which accountants practice is becoming increasingly complex.
This complexity is evident in all fields of accounting including management accounting,
financial accounting and auditing. In these fields, accountants make decisions that involve
consideration of multiple conflicting and incommensurable dimensions. Accountants need
to ensure that they effectively consider these factors in arriving at their decisions concerning these complex problems. The Goal Programming (GP) Model was widely utilized
for dealing with accounting decision-making situations. Different GP variants have been
applied within the fields of management accounting, financial accounting and auditing.
The aim of this paper is to propose a typology that will serve as a guideline to identify the
most appropriate variant of GP to deal with specific accounting related decision-making.
Keywords: Applications of MCDM, Goal Programming, Management Accounting And
Auditing, Preference Information.
201
TU1A (plenary)
Goal Programming: An Overview of Current
Developments and an Application to Offshore Wind
Farm Modelling
Jones, Dylan (*)
University of Portsmouth
This seminar will present the current state-of-the-art of the goal programming technique.
An overview of currently proposed variants and their linkage to underlying decision-maker
philosophies will be given. The use of goal programming as a flexible modelling paradigm
rather than a specific computational technique will be discussed. The combination and
integration of goal programming with other modelling techniques from artificial intelligence and the wider field of multi-criteria decision making and Operational Research will
be detailed. The use of meta-objectives to incorporate a mixture of underlying decisionmaker philosophies will be outlined. A modelling framework for this purpose will be
developed. The second part of the seminar will be concerned with the application of
Multi-Criteria Decision Making methodologies to the logistics problems arising during
the life cycle of an offshore wind farm. The different stakeholders and their preferences
will be outlined. Operational and strategic goals, objectives, and constraints will be formulated. The methodology outlined in the first section of the talk will be used in order
to build a model that can be used to explore consensus solutions. The results will be
discussed and conclusions drawn.
Keywords: Goal Programming, Logistics, Offshore Wind Farm, State Of The Art.
202
TU3F (invited)
GP-DEMO: Differential Evolution for Multiobjective
Optimization Based on Gaussian Process Models
Mlakar, Miha (*) Filipiˇc, Bogdan Petelin, Dejan
Joˇzef Stefan Int. Postgraduate School
Tuˇsar, Tea
This paper proposes a novel surrogate-model-based evolutionary multiobjective algorithm
called Differential Evolution for Multiobjective Optimization Based on Gaussian Process
Models (GP-DEMO). The algorithm builds upon the newly defined relations for comparing solutions under uncertainty that extend the Pareto dominance relation. These newly
defined relations, due to the use of a confidence interval of the approximation in addition
to the approximated value, minimize the possibility of wrongly performed comparisons of
solutions due to inaccurate surrogate model approximations. The GP-DEMO algorithm
was tested on several benchmark problems and two computationally expensive real-world
problems. To be able to assess the algorithm performance, we compared it with another
surrogate-model-based algorithm called Generational Evolution Control (GEC), which
does not consider uncertainty when comparing solutions, and Differential Evolution for
Multiobjective Optimization (DEMO), which does not use surrogate models, but is known
to be very effective. All three algorithms use the solution creation techniques from the
Differential Evolution algorithm, and both GP-DEMO and GEC rely on Gaussian process
modeling for building surrogate models. The DEMO algorithm was used for assessing the
quality of the results gained with GP-DEMO, while the comparison between GP-DEMO
and GEC was carried out to test how the new relations affect the number of exact solution
evaluations needed during the optimization process. The analysis of the results over all
test problems shows that the quality of the results obtained with GP-DEMO is similar to
the results obtained with DEMO. The number of exactly evaluated solutions performed
with GP-DEMO was similar to GEC, but the quality of the results obtained with GPDEMO was higher, mainly due to wrongly performed comparisons of the inaccurately
approximated solutions in GEC.
Keywords: Differential Evolution, Evolutionary Algorithm, Gaussian Process Modeling, Multi-Objective Optimization, Surrogate Modeling, Uncertain Pareto Dominance.
203
WE1E (invited)
Green Economy in the State of Rio de Janeiro - A
Multicriteria Evaluation
Valle, Rogerio (*)
Federal University of Rio de Janeiro
Cl´ımaco, João
INESC Coimbra-Universidade de Coimbra
Rio de Janeiro commissioned a methodology for measuring the State’s Green Economy
level. Economic, social and environmental aspects were prioritized and potential performance partial indicators were identified. It was decided to use data for each industry.
Available databases provided our final set of partial indicators, which allowed measuring
the multidimensional performance of each industry. It was decided not to aggregate the
set of partial indicators into a single Green Economy index. For each partial indicator,
thresholds were defined determining the tolerance range in the classification of industries
performance. The aggregation among the dimensions, looking for a global classification of
the industries, was done utilizing an interactive version of the conjunctive method. The
SABILOC-MAT 1.0 package enables a clear communication among all the actors involved
in the process. A dashboard was built enabling a simple and interactive sensitivity analysis.
Keywords: Multi-Criteria Decision Analysis, Sustainability.
204
MO3C (contributed)
Grey Numbers and Multi-Criteria Decision Making
Pearman, Alan (*)
University of Leeds
Xie, Naiming
Nanjing University of Aeronautics and Astronautics
This paper is concerned with the standard Multi-Attribute Decision Making (MADM)
model [m alternatives; n evaluation attributes, each with a weight, w; choice by maximising linear weighted sum of the scores]. The MADM model is typically an approximation.
Approximation may stem from doubts whether all relevant attributes have been identified, about preference structures, presence of risk, and lack of confidence about accuracy
of weights and/or scores. In this paper we focus on the last two of these. A standard
approach in the face of such uncertainty has been to run the basic MADM model and then
apply sensitivity testing. A second has been to recognise uncertainties explicitly from the
outset, undertaking the analysis using devices to permit imprecise inputs, such as interval
bounds on parameter values, or fuzzy numbers. An important recent innovation of this
kind has been the development of grey analysis, following the introduction of grey systems
theory (GST) in 1982 by Professor Deng. As the basic element of GST, grey numbers
are defined as uncertain numbers such that the certain value is not known, although the
potential value set is known. There are now a considerable number of papers outlining
different grey approaches to multi-criteria decision making. In this paper we redefine the
concept of grey number, outline the main features of grey analysis, review the range of
grey multi-attribute choice models, identify strengths and weaknesses and compare the
implementation of these grey models with others that have broadly similar aims. Then
we create a new grey decision-making model by combining grey numbers with the MADM
model. Assessment of the differences emphasises especially a behavioural focus, highlighting what sorts of judgement errors unaided decision makers might be most likely to make
and what sorts of modelling support are best placed to help them avoid perceptual errors
and have justifiable confidence in the choices they make.
Keywords: Grey Numbers, Interval-Valued Functions, Multi-Criteria Decision Analysis.
205
TH2F (invited)
Habitat Availability and Spatial Quality as Conflicting
Objectives of Multi-use Forest Management
Podkopaev, Dmitry (*) Mazziotta, Adriano Miettinen, Kaisa
Mönkkönen, Mikko Reunanen, Pasi
Juutinen, Artti
University of Oulu
Tikkanen, Olli-Pekka
University of Eastern Finland
We consider a multiple-use forest management problem where goals are to maximize net
present value of timber production and ecological quality of the forest landscape evaluated
for several representative species. For each species we consider separately two indicators:
habitat availability and landscape quality related to spatial arrangement of the habitat. This leads to formulation of a multi-choice knapsack problem with multiple linear
and quadratic objectives. We address challenges of solving the problem for large forest
landscapes and analyze trade-offs between habitat availability and quality of the habitat
structure.
Keywords: Combinatorial Optimization, Forest Management, Knapsack, Land Use Planning, Spatial Multi-Criteria Evaluation.
206
TH2E (invited)
Handling Computational Variability in the Evaluation
of Objectives: Ideas and Prospects
Handl, Julia (*)
Knowles, Joshua
Lavygina, Anna
King’s College, London
Our research focuses on multiobjective optimization scenarios in which the different objective functions have different computational or temporal costs, as may be the case e.g.
in experimental optimization problems. Such scenarios may pose several challenges to
algorithm design including (i) missing objective values, (ii) maintaining a set of solutions
at once (e.g. to assess diversity or compute performance metrics) as more expensive objectives need longer to be evaluated, and (iii) integration of user preferences hampered
by inaccurate objective values. Our previous work concluded that waiting for expensive
evaluations to complete is the preferred choice when waiting periods are short, and whilst
an EA augmented with a fitness-inheritance based approach for dealing with missing objective values performed well, we believe that other strategies may fare better, including
surrogate-modelling approaches (e.g. Kriging) and strategies that schedule intelligently
the evaluation of solutions on expensive objectives so as to maximize expected gain in
information. Alternatively, EAs could be extended with reinforcement learning to learn
when to switch between waiting and not waiting, and different strategies for dealing with
missing values. To address (ii) one may allow mixing of generations so that solutions
residing in an archive are getting progressively more accurately evaluated. Refining user
preferences (interactively) as more expensive objective values become available can be an
option to cope with (iii). To establish a common ground and facilitate algorithm design
it seems to be worthwhile to define a general framework for describing algorithms that
can deal with computationally variable objectives. This, combined with our empirical
experience could form a good basis for more theoretical work on convergence and/or drift
effects. on the simple algorithm models. Delayed constraints or resource constraints causing delays may also be considered in future.
Keywords: Evolutionary Multi-Objective Optimization, Multi-Objective Optimization,
Non-Regular Problems, Preferences.
207
TU5C (contributed)
How to Support Environmental Management by
MCDM Techniques?
Reichert, Peter (*)
Eawag
Langhans, Simone
Eawag
Lienert, Judit
Eawag
Schuwirth, Nele
Eawag
Environmental decision support intends to use the best available scientific knowledge to
help decision makers in finding management options that fulfill societal objectives to the
degree possible. This requires a careful analysis of (i) how scientific knowledge can be
formally represented and quantified, (ii) how societal preferences can be described and
elicited, and (iii) how these theoretical concepts can best be used for communication with
authorities, politicians, and the public. A multitude of MCDM methodologies is available
to support these processes. We analyze and discuss which of these are conceptually most
satisfying for this purpose and how practical limitations can be overcome. With respect to
(i) we suggest the use of intersubjective probabilities to describe the current state of scientific knowledge. To address (ii), we suggest using value and expected utility theories to
describe societal preferences. With respect to (iii) we outline a well-structured procedure
for environmental decision support, exemplified for the case of river management. Key
elements of this approach are the in-depth discussion, hierarchical structuring and quantification of objectives, the use of probabilistic scientific predictions, and the combination
of these two elements for ranking and analyzing decision alternatives. This approach is
complemented by visualization techniques to support transparent communication. We
discuss conceptual arguments in favor of the suggested approach, its limitations, and first
experiences of transferring it into river management practice in Switzerland.
Keywords: Bayesian Analysis, Decision Support, Environmental Decision Making, MultiAttribute Utility Theory, Uncertainty Modelling.
208
MO5E (award)
Hybrid Evolutionary Multi-Objective Optimization for
Enhanced Convergence and Diversity
Sindhya, Karthik (*)
Evolutionary multi-objective optimization (EMO) algorithms are often criticized for their
slow convergence, the lack of a theoretical convergence proof and for having no efficient
termination criterion. In our research, the main focus is to improve EMO algorithms
by addressing these criticisms. Hybrid EMO algorithms defined as hybrids of EMO algorithms and a local search procedure are proposed to overcome the criticisms of EMO
algorithms. In the local search procedure, a local search operator originating from the
field of MCDM (involving solving an achievement scalarizing function using an appropriate mathematical optimization technique) is used to enhance the convergence speed of a
hybrid EMO algorithm. A hybrid framework, a base on which hybrid EMO algorithms
can be built, is also proposed incorporating a local search procedure, an enhanced diversity preservation technique and a termination criterion. As a case study, a hybrid EMO
algorithm based on the hybrid framework is successfully used to find Pareto optimal solutions desirable to a decision maker in the optimal control problem of a continuous casting
of steel process. In addition, a hybrid mutation operator consisting of both nonlinear
curve tracking and linear differential evolution mutation operators is proposed to handle
various interdependencies between decision variables in an effective way. The efficacy of
the hybrid operator is demonstrated with extensive numerical experiments on a number
of test problems. Furthermore, a new progressively interactive evolutionary algorithm
(PIE) is proposed to obtain a single solution desirable to the decision maker. Here an
evolutionary algorithm is used to solve scalarizing functions formulated using the preference information of the decision maker. In PIE, the decision maker moves progressively
towards her/his preferred solution by exploring and examining different solutions and
does not have to trade-off between the objectives.
Keywords: Achievement Scalarizing Functions, Differential Evolution, Hybrid Framework, Interactive EMO, Multi-Criteria Decision Making, Mutation, NAUTILUS Method,
NSGA-II.
209
MO3D (contributed)
Hydrological Constraints in Optimal Timber Harvest
Scheduling Problems: A Case Study in Eucalyptus
Plantations
Giménez, Juan Carlos (*)
Universidad de Extremadura
Bertomeu Garc´ıa, Mercedes
Universidad de Extremadura
D´ıaz-Balteiro, Luis
This work describes a multiple criteria decision-making (MCDM) approach which forest
managers can use to evaluate the potential impact of timber harvests on long-term forest
management strategic objectives. We proposed a sequential procedure under a multicriteria framework to address sustainable management in industrial forest plantations
taking into consideration hydrological constraints to reduce erosion risk. The approach is
based upon a strategic forest-level planning model and Geographical Information System
(GIS) data. First, a timber harvest scheduling model consistent with multiple objectives
commonly identified in forest management plans for this type of plantations was formulated. Hydrological constraints were formulated to ensure erosion control for each stand.
This formulation is based on GIS layers as slope, flow accumulation and LS factor (topographical USLE factor) obtained through spatial analysis. Then, a set of sustainability
indicators was identified and briefly described. Next, several management alternatives
were generated and assessed in terms of the sustainability indicators’ values. Finally, the
management alternatives were ranked using a goal programming methodology by which
the different indicators are properly aggregated. Furthermore, different priority rankings were obtained, which could represent potential preferences of the decision maker.
The procedure was applied to a Eucalyptus globulus Labill. plantation in northwestern
Spain. The results showed the validity of this approach, thus deriving the most sustainable management alternative under each scenario. Moreover, this methodology allows for
consideration of different preferential weights for the sustainability indicators previously
defined. In a further work, we plan to develop a tactical model to identify an annual
silvicultural schedule map, i. e., we will try to precisely locate harvest areas in each stand
under additional erosion control constraints.
Keywords: Erosion Control, Eucalyptus Plantations, Goal Programming, Harvest Scheduling.
210
TU5F (contributed)
Identifying Supported and Extreme Supported
Nondominated Solutions and an Empirical Analysis of
their Representative Quality
Sayin, Serpil (*)
Koc University
Multiple Objective Discrete Optimization (MODO) problems are known to be burdened
by the existence of unsupported efficient solutions. Previous work has demonstrated that
supported efficient solutions, which are relatively easy to obtain, constitute only a small
fraction of the efficient solutions, especially for larger problems. Obtaining the set of
supported nondominated solutions has been a natural first step towards obtaining the
entire set of nondominated solutions in some MODO approaches. To date, the quality of
the set of supported nondominated solutions as a representation has not been thoroughly
investigated. In this study, we investigate the representation quality of the set of supported nondominated solutions and the set of extreme supported nondominated solutions
on the multiobjective knapsack and assignment problems. We obtain the entire efficient
nondominated set using an epsilon-constraint-based approach. Given this set, we propose
two simple linear programming formulations to identify the supported nondominated solutions and extreme supported nondominated solutions respectively. We experiment with
different data generation schemes and different problem sizes. We observe that the set of
supported nondominated solutions is almost always a good representation of the entire
nondominated set. Our results indicate that even extreme supported nondominated solutions provide a good representation of the entire efficient set. We conclude that more
research emphasis may be placed on obtaining supported nondominated solutions and
extreme supported nondominated solutions due to the relative computational ease of obtaining them.
Keywords: Multi-Objective Optimization, Non-Dominated Set, Representation Quality, Representative Set, Supported Non-Dominated Solution.
211
MO5H (poster)
Impact of Food Price Volatility over Irrigation
Agriculture using a Revealed Preference Model
Gutierrez-Martin, Carlos (*)
Berbel, Julio
Gómez, Carlos Mario
University of Alcalá
In this paper we show a simulation tool which uses mathematical programming methods to reveal the implicit multiattribute objective function lying behind the observed
cropping decision. The model takes different criteria such as profit maximization, risk
aversion, avoidance of management complexities and so forth into account. In order to
determine the feasible combination of attributes of this objective function the model considers the production possibility frontier explicitly as depending on market prices, policy
incentives, availability of production factors, water irrigation facilities, agronomic vocation and other constraints. Once calibrated the model becomes a powerful tool to assess
the impact of different scenarios such as crop prices volatility, water prices modifications,
irrigation technique substitution and so on. The objective of this document is to identify
and quantify the effects of price volatility over the cropping patter and the water use.
Other relevant variables such as agricultural income and employment are also considered.
To do so we develop a revealed preferences model that uses basic microeconomic theory
to calibrate and simulate farmers’ preferences. This kind of models present at least two
advantages compared to the more commonly used Positive Mathematical Programming
(PMP) (Heckelei and Britz, 2005; De Frahan et al., 2007) and Multi Criteria Decision
Models (MCDM) (Berbel and Rodr´ıguez-Oca˜
na, 1998; Berbel and Gómez-Limón, 2000;
Gómez-Limón and Riesgo, 2004): first, they do not assume linear preferences; and secondly, they do not assume implicit cost functions that are not observable, being both the
main criticism against PMP and MCDM. The model is supported by a data base built on
purpose for its implementation covering all Guadalquivir River Basin (Southern Spain)
with high spatial detail, although we only presents the results of one of the typologies
found in a cluster analysis.
Keywords: Decision Maker’s Preferences, Price Volatility, Revealed Preferences.
212
TU5F (contributed)
Improved Constraint Handling Technique for
Maheshwari, Vaibhav (*) Rangaiah, Gade Pandu Sharma, Shivom
National University of Singapore
Penalty function and feasibility approaches are the two popular constraint handling techniques for solving constrained MOO problems by stochastic global optimization methods. In general, feasibility approach works well for solving problems with inequality
constraints. It gives higher priority to a feasible solution over an infeasible solution, but
limits the diversity of search. Feasible search space is extremely small for equality constrained problems, and so feasibility approach may not be effective for solving problems
with equality constraints. In this work, an adaptive relaxation of constraints along with
feasibility approach is explored for solving constrained MOO problems by a stochastic
optimizer. Here, all constraints are dynamically relaxed, which makes certain individuals temporarily feasible during selection of individuals for the next generation. The
adaptive relaxation of constraints along with feasibility approach is incorporated in the
multi-objective differential evolution algorithm, and its performance is compared with
that of feasibility approach alone. It is first tested on two MOO test problems with equality constraints. Then, the proposed approach is applied to optimize the performance of
two fermentation processes with many equality constraints. It is found that the proposed
approach works better than the feasibility approach with a user defined relaxation value
for equality constraints. However, both these simultaneous solution approaches perform
inferior compared to sequential solution of optimization problem and equality constraints,
on both the applications. Sequential approach requires numerical techniques for solving
highly complex and nonlinear process model equations, and these techniques may require
good initial estimates. Conversely, simultaneous solution approach does not require any
additional information. In this work, the proposed approach will be described, and will
be compared with the feasibility and sequential approaches.
Keywords: Adaptive Relaxation Of Constraints, Constraint Handling, Feasibility Approach, Multi-Objective Differential Evolution, Sequential Solution Approach, Simultaneous Solution Approach.
213
TH2B (contributed)
Improved Upper Bounds for a Two-Phase Biobjective
Shortest Path Algorithm
Medrano, F. Antonio (*) Church, Richard
University of California Santa Barbara
The biobjective shortest path (BOSP) problem is an extension of the single-objective
shortest path problem, in that it simultaneous considers optimizing two competing objectives. Rather than solving for a single optimum, the BOSP solves a set of paretooptimal paths that represent the best trade-offs between the two objectives. This has
been shown to be NP-hard. Raith and Ehrgott (2009) compared the existing state-of-theart approaches for solving BOSP problems, including label-correcting and label-setting
methods, and a new enumeration method using a Near Shortest Path (NSP) algorithm
from Carlyle and Wood (2005). They also proposed two-phase variants of the three approaches that use a polynomial technique for determining supported solutions to define
an upper-bound on the unsupported solution search. We have developed a new algorithm
based on the two-phase NSP method proposed by Raith and Ehrgott. Our new method
sets a tighter initial upper bound by generating gateway paths (Lombard and Church
1993) during the supported solution computation (Cohon et al. 1979). With minimal additional computation, the gateway heuristic generates a set of paths that are near-optimal
to the paretal set. This tighter upper bound reduces the NSP computation, resulting in
faster overall run-times. We discuss the details of this approach, and results comparing
it to other methods on various networks. Carlyle, W.M. & R.K. Wood, (2005). Nearshortest and k-shortest simple paths. Networks, 46, 98-109. Cohon, J.L., R.L. Church
& D.P. Sheer, (1979). Generating multiobjective trade-offs: An algorithm for bicriterion
problems. Water Resources Research, 15, 1001-1010. Lombard, K. & R. Church, (1993).
The gateway shortest path problem: Generating alternative routes for a corridor location
problem. Geographical Systems, 1, 25-45. Raith, A. & M. Ehrgott, (2009). A comparison
of solution strategies for biobjective shortest path problems. Computers & Operations
Research, 36, 1299-1331.
Keywords: Corridor Location, Multi-Objective Optimization, Multi-Objective Shortest Path Problem, Shortest Path.
214
TU3G (contributed)
Incorporating Stakeholders’ Preferences into
Wastewater Infrastructure Planning in Switzerland
Zheng, Jun (*)
Eawag
Lienert, Judit
Eawag
We consider a complex decision problem aiming at planning wastewater infrastructures
until 2050 in Switzerland. The planning has long-term environmental, economic and social
consequences, as assets are expensive and long-lived. Incorporating preferences of stakeholders representing different social sectors into the decision process enables them to get
an integrated perspective and ultimately helps to reach a consensus. Eliciting stakeholders’ preferences with Multi-attribute utility theory (MAUT) can be cognitively demanding
for the stakeholders whose ambiguous value judgments are to be quantified. We tackle
the difficulty by (1) using adapted interval SWING method which elicits weights of objectives in an imprecise way; (2) decomposing weight elicitation questions into four types of
simple questions, which significantly reduce their cognitive burden so that the weights are
obtained in a reliable way; (3) allowing stakeholders to express preferences with verbal
scales that are later translated to numbers, rather than directly asking for numbers; (4)
using structured and personalized questions to elicit value functions and risk attitudes of
their most important objectives, and their attitudes for tradeoffs which determine their
own aggregation models. The questions are designed based on behavioral decision theory to overcome common biases. They are implemented by an online questionnaire and
face-to-face structured interviews with 10 important stakeholders. We reveal different
stakeholders’ preferences towards the objectives of wastewater infrastructure planning.
Preference programming technique then identifies the most robust alternative as a “best”
alternative for all stakeholders using the elicited imprecise preferences. The proposed
elicitation procedure is applied in the context of wastewater infrastructure planning, but
is widely applicable when the stakeholders’ preferences for complex decision problems are
to be incorporated into MAUT framework.
Keywords: Environmental Decision Making, Preferences, Utility Theory, Water Infrastructure Planning, Weights.
215
TU4C (contributed)
Incorporation and Communication of Uncertainty in
Decision Support for Environmental Management: A
Case Study on Water Quality Improvement
Schuwirth, Nele (*)
Eawag
Reichert, Peter
Eawag
Stamm, Christian
Eawag
We illustrate methods to incorporate uncertainty into environmental decision support processes with a case study on the improvement of water quality in a river catchment of the
Swiss Plateau. Different management options to reduce point and non-point sources are
evaluated in this study. We apply the multi-attribute value and utility theory to evaluate
alternatives and demonstrate how methods of sensitivity and uncertainty analysis can be
used to facilitate the decision making process. Uncertainty in prediction of consequences
formally requires the consideration of risk attitude in addition to the strength of preference and therefore switching from value to utility functions. However, the elicitation of
utility instead of value functions has major disadvantages and is much more demanding.
In complex decision situations with many sub-objectives this might not be feasible. We
examine under which conditions risk attitude affects the ranking of alternatives and when
the information contained in value functions is sufficient and even superior for decision
support. Along with the case study, we introduce the software package ’utility’ (implemented in the statistics and graphics environment R) that facilitates the visualization of
uncertain valuations in complex decision situations. The package allows its users to define
arbitrary aggregation functions, to formulate multi-attribute and conditional end nodes,
to switch from values to utilities at any level of the objectives hierarchy, and to extend
the decision support framework by arbitrary types of nodes. With these features it is intended to facilitate research on the construction of value and utility functions beyond the
additive model that appears to be inappropriate in certain environmental management
situations.
Keywords: Environmental Decision Making, Multi-Criteria Decision Analysis, Risk, Uncertainty Modelling, Utility Theory.
216
TH3C (contributed)
Increasing the Transparency of Model Selection in
Multi-Criterion Data-Clustering
Handl, Julia (*)
Papamichail, K.Nadia
State-of-the-art systems for decision-support typically include functions to explain and
to justify the decisions recommended by the system (Papamichail & French 2003). Capabilities of this type have been shown to play an essential role in informing decision
makers and in ensuring the adoption of the recommendations made by the system. In
this work, we consider the potential use of similar approaches in the context of multicriterion methods for data clustering (Handl & Knowles 2007). In particular, we are
looking at the automatic generation of contextual explanations and justifications using
natural language generation techniques to support the comparison and selection of representative solutions in multi-criterion clustering, and increase the transparency of existing
approaches. Methods for data-clustering use intrinsic data properties to partition large
data sets into clusters, with the ultimate aim of identifying groupings that are meaningful
and interpretable by an expert user. Strategies to support decision makers in this last
step have received little attention in the literature. Generally, no attempt has been made
to support contextual comparisons between sets of different possible clustering solutions.
This is despite the fact that sets of fundamentally different solutions are commonly encountered as the end result of a cluster analysis: this may be as a consequence of the
use of various clustering algorithms, different parameter settings, or due to multiple clustering objectives. Solutions within the final set of candidate partitionings will typically
differ with respect to the number of clusters, the level of discrimination along individual
features, the scores for specific clustering criteria and their mutual similarity. Our aim is
to develop and present a system that integrates such information and facilitates contextual comparisons between solutions. This would enable experts to make more informed
decisions about their preferred clustering solutions
Keywords: Expert systems, Multi-Criteria Decision Analysis.
217
MO5H (poster)
Induction of Belief Rules and Belief Decision Trees
from Uncertain Data
AbuDahab, Khalil (*)
Chen, Yu-Wang
Xu, Ling
In this paper, we present and evaluate an induction algorithm for acquiring belief rulebases, by inductive inference, from uncertain data. Existing methods are able to extract
traditional rules (with consequents that are believed to be either 100% true or 100% false)
inductively from certain data. The proposed method extracts belief rules from data which
may contain uncertain or incomplete knowledge in both antecedent and consequent attributes. Belief rules can capture uncertain or incomplete knowledge using uncertain belief
degrees in consequents. Instead of using singled-value consequents, each belief rule deals
with a set of collectively exhaustive and mutually exclusive consequents. The method is
also able to handle hierarchically structured attributes. A MCDM aggregation method is
used to aggregate attributes then only the aggregated attributes are included in the rules
and used during inference process. A number of aggregation methods have been evaluated
to find the most appropriate one for calculating an aggregated information entropy. The
evidential reasoning (ER) approach was able to handle multiple, qualitative or quantitative, attributes under uncertainty while other methods fail to do so. These features
allow to minimise tree size in order to avoid over-fitting the training data and poorly
generalising to new samples. They also avoid producing a larger number of rules which
can make the rule-based reasoning system slow. A generic expert system was developed
in order to evaluate the algorithm. The system allows rule-based classification models
to be created by acquiring knowledge inductively from training examples or deductively
from experts in the problem domain. Building belief rule based models inductively from
data and deductively from domain knowledge is important when domain experts have
sufficient knowledge of some, but not all, aspects of a problem.
Keywords: Belief Decision Trees, Classification, Rule Extraction, Rule-Based Knowledge Acquisition, Supervised Learning.
218
TH3B (invited)
Inference of Parsimonious Preference Models in
Robust Ordinal Regression
Slowinski, Roman (*)
Greco, Salvatore
Mousseau, Vincent
Ecole Centrale Paris
Ordinal regression is used to infer parameters of a supposed preference model, which
make this model compatible with preference information provided by the Decision Maker
(DM). As values of these parameters are usually not unique, robust ordinal regression
handles a complete set of parameters of the compatible preference model of a given class
(e.g., additive value function, like in UTAˆGMS method, or outranking relation, like in
ELECTREˆGKMS or PROMETHEEˆGKS methods). Here we care not only about compatibility of the model with the preference information, but also about keeping the model
as parsimonious as possible. In this context, “parsimonious” means simple and intuitive.
We achieve this goal by solving two linear programming problems that permit to find the
“most linear” value function able to represent the considered preferences, as well as the
value function composed of the minimal number of linear pieces, respectively. These two
linear programming problems have a solution under condition that it is possible to represent the DM’s preference information using an additive value function. If this is not the
case, some more complex value function can be considered, for example, involving synergy
components for pairs of criteria, as in the UTAˆGMS–INT method. Also in this case, one
can apply the above approach in order to search for a parsimonious model, i.e. “most
linear” marginal value functions with the least number of pairwise synergy components.
Keywords: Elicitation, Interaction Among Criteria, Multi-Criteria Decision Aid, Preference Learning, Preference Modeling, Robust Ordinal Regression.
219
TU2E (invited)
Information Levels in Additive Group Decision Models
under Incomplete Information: Bridging the
Cardinal-Ordinal Gap
Dias, Luis (*)
University Coimbra
Sarabando, Paula
IP Viseu - INESC Coimbra
Vetschera, Rudolf
University of Vienna
Aggregation of cardinal values in groups requires group members to specify their preferences in cardinal form, by assigning utility scores to alternatives. This could be difficult
for group members, in particular if this information should be provided exactly. Methods
for decision making under incomplete information like robust ordinal regression could be
employed to simplify this task. Such methods allow information to be specified also only
in ordinal form (e.g. as a ranking); this information can then be used to approximate
a cardinal utility function. Along the spectrum between purely cardinal and purely ordinal information, group members could also specify preference information not only as
a ranking of alternatives, but also as a ranking of differences of alternatives. Obviously,
providing information closer to exact cardinal utility values will increase the precision
of preference statements at the group level, but the strength of this effect is not clear.
Besides each individual member’s incompletely known preference function, the weighting
of these functions is also unknown. A recent paper extended Arrow’s non-dictatorship
condition to the aggregation of cardinal preferences in a way that implies a constrained
set of weights of group member in an additive group utility function. In this paper, we
report on a comprehensive computational study, in which we attempted to quantify the effect of providing different levels of preference information (exact cardinal values, rankings
of differences between alternatives and rankings of alternatives), and of the imposition
of different constraints on members’ weights in an additive group utility function (equal
weights, general random weights and random weights satisfying the Non-dictatorship condition) on outcomes at the group level. Relevant outcome dimensions analyzed refer to
the structure of the group preference relation (necessary and possible relations) and the
strength of impact of individual group members.
Keywords: Group Decision Making, Incomplete Information, Robust Ordinal Regression.
220
MO4E (contributed)
Integrating Qualitative Assessment and Fuzzy AHP to
Prioritize Investments in an Energy Efficiency
Program of Favelas in the City of Rio de Janeiro
Ribas, Jose Roberto (*)
Rocha, Mariana
Light Servi¸cos de Eletricidade S/A
Delta Energia
This study proposes an integrated qualitative and quantitative assessment of expert opinions aiming at rank a set of five favelas located in the city of Rio de Janeiro candidates
for investments in an energy efficiency program implemented by the local electric utility
company. The city wants to get rid of crime and present itself as a peaceful metropolis
to host two big sports events: the soccer’s World Cup in 2014 and the Olympic Games in
2016. In the past years some favelas were chosen to be prototype for an ambitious project
to reshape Rio de Janeiro, known by its acronym, UPP (Unidade de Policia Pacificadora),
which means Pacifying Police Unit. This involves sending in special tactics police (BOPE)
to drive drug traffickers out, then installing the UPP in the favela. Once security has been
established is possible to improve general living conditions in these areas. The core of
the energy efficiency program was to convert informal customers to formal ones, as far as
such communities were responsible for approximately 40% of the commercial loss (stolen
energy) in the city. The model specification presented in this paper was set up with
ten relevant criteria for decision making identified through an in-depth interview with
managers. The relative importance of the criteria and the performance of each favela regarding every criterion were measured by SIMOS method. The preferences resulting from
this method were translated to a nine-point scale. The imprecision of subjective judgment
was partially compensated by using a fuzzy analytical hierarchy process (FAHP). Some
criteria were ordinal, such as “Fair Relationship with the Community” and “Complexity
to Rebuild the Distribution Lines”, other were cardinal, like “Percentage of Clients in
Default” or “Commercial Loss Due to Energy Theft”. At the end, the model was efficient
on ranking the five favelas, therefore, contributing to a rational and transparent approach
for capital investment in social projects.
Keywords: Energy Efficiency, FAHP, Favelas, Prioritizing Objectives.
221
FR2C (invited)
Learning the Parameters of a Multiple Criteria Sorting
Method from Large Sets of Assignment Examples
Sobrie, Olivier (*)
Mousseau, Vincent
Pirlot, Marc
In multiple criteria decision analysis (MCDA), the sorting procedures aim at assigning
each alternative of a set into one of the pre-defined and ordered categories. We consider
in this study a derived version of the original ELECTRE TRI sorting procedure. This
simplified version of the ELECTRE TRI procedure, proposed and studied by Bouyssou
and Marchant, assigns the alternatives into categories on the basis of a majority rule. We
call it MR-Sort procedure. A difficulty with the MR-Sort procedure is the elicitation of
its parameters. An MR-Sort model composed of n criteria and p category requires the
elicitation of p - 1 profiles, n weights and a cut threshold. For the decision maker, eliciting explicitly these parameters is not trivial. This is why several papers deal with the
learning of parameters of such models on the basis of assignment examples. Currently,
the proposed algorithms are not suitable for large problems, i.e. problems involving a
lot of learning examples, categories and/or criteria. This is mainly due to the computing
time that becomes important when the number of parameters increases. We propose a
metaheuristic to learn the parameters of a MR-Sort from a set of assignment examples
and their performances vectors. The metaheuristic is composed of two main parts. The
first part aims at learning the weights and the cut threshold and the second one aims at
learning the profiles. The algorithm first generates a random set of profiles. Then a linear
program is used to learn the weights and the cut threshold on basis of the random generated profiles. After learning the weights, a metaheuristic adjusts the profiles evaluations.
The metaheuristic tries to maximize the number of compatible examples with the learned
model. Our research includes a set of experiments studying the algorithm performances,
the behavior of the metaheuristic in presence of errors in the assignment examples and
the algorithm capability to find models restoring the input examples.
Keywords: ELECTRE Methods, Linear Programming, Metaheuristics, Multi-Criteria
Sorting, Preference Learning.
222
WE2G (invited)
Location Decision for Firm Expansion: A Bicriterion
Approach
Pelegr´ın, Blas (*)
Garc´ıa Pérez, Mar´ıa Dolores
Universudad Católica San Antonio
Location is an strategic decision for an expanding firm in competition with other existing
firms. We present different location models in which two objectives are considered. The
first is maximizing the market share (or profit) obtained by the expanding firm, once its
new facilities are located. The second is minimizing the lost of market share (or profit) of
its old facilities caused by the new facilities (called the cannibalization effect). Customers
chose a facility according to a patronizing behaviour depending of each location model.
Some formulations are presented which let generate Pareto solutions by solving integer
mixed linear programming problems.
Keywords: Decision Analysis, Location, Mixed Integer Programming.
223
MO2G (contributed)
Markowitz’s Investment Problem under Multicriteria,
Uncertainty and Risk
Korotkov, Vladimir (*)
Belarusian State University
Emelichev, Vladimir
Belarusian State University
We consider a multicriteria Boolean discrete variant of Markowitz’s investment problem
with Savage’s bottleneck risk criteria. Such model demonstrates the way how an investor,
choosing the right portfolio, can minimize the risk of losing own gain, given some certain
level of expected income. Under such formulation, the initial data are given as static
expert evaluations of risks (financial, environmental and similar). It is well-known that
calculating these values is usually accompanied by a large number of computational errors which lead to a high degree of uncertainty of the initial data. Thus, there is a need
to take into account uncertainty and inaccuracy of the initial data for the investment
problems like this, because even small changes of initial data make a model behave in an
unpredictable manner. One of the crucial questions arising in uncertainty analysis is the
question about extreme level of the initial data variations (perturbations) preserving optimality of a chosen portfolio. We investigate quantitative estimates of permissible changes
of the uncertain initial date, which preserve the Pareto optimality of a portfolio using
the Hölder metric in the perturbed parameters space. As a result we present attainable
(unimprovable) lower and upper bounds for the stability radius.
Keywords: Hölder Metric, Multi-Criteria Investment Problem, Pareto Optimal Investment Portfolio, Savage’s Criteria, Stability Radius Of Portfolio.
224
WE1E (contributed)
MCDA-GIS Integration for the Evaluation of the
Invasive Alien Species. The Case of the Eastern Grey
Squirrel in Umbria (Central Italy)
Rocchi, Lucia (*) Massei, Gianluca Paoloni, Daniele
University of Perugia
Multicriteria Decision Analysis (MCDA) methods are a basic tool in the field of environmental
valuation, but they cannot take into account the spatial dimension easily. The conventional
MCDA approach assumes spatial homogeneity of alternatives within the case study area, although this is unrealistic. To face a spatial decision problem means to combine Geographical
Information Systems (GIS) and MCDA approach [2]. Value judgments, geographical data, their
transformation and elaboration are needed to solve it[2]. Several integrations are possible, but
only with a complete one [2] the two systems share the same interface and database. An example
of complete integration is the r.mcda suite [3], we used in this work. The suite is a modular
MCDA package developed in GRASS GIS 6.4, including several multicriteria methods. We applied the DRSA [1], for understanding the possible damage to agriculture crops by the grey
squirrel. The Eastern grey squirrel (Sciurus carolinensis) is a North American species that was
introduced in UK and Italy. Its impact on European ecosystems is very strong, in particular on
the native red squirrel. Grey squirrels are able to compete more successfully than the reds ones
for food and habitat. This competitive exclusion leads to the extinction of the native species
[5]. Grey squirrels can cause damages also to agroecosystems, with heavy consequences for agriculture [4]. The aim of the work is to identify risk areas for high quality crops, related to the
potential spreading of grey squirrel in Umbria (Italy). Several hotbeds have been identified, and
without proper management and control actions by public bodies, the matter of Grey squirrel
is destinated to blow up.
[1]GRECO S., MATARAZZO B., SLOWINSKI R. 2001. Rough sets theory for multicriteria decision analysis. EUR J
OPER RES 129, 1-47.
[2]MALCZEWSKI J. 2006. Gis- based multicriteria decision analysis: a survey of the literature. IJGIS 20, 703-726.
[3]MASSEI G, ROCCHI L., PAOLOTTI L., GRECO S., BOGGIA A. 2012.MCDA GIS integration: an application in
GRASS GIS 6.4. In OGRS- Symposium proceeding O. Ertz, S. Joost and Tonini M., (Eds) (2012) pp 195-201.
[4]SIGNORILE A.L., EVANS J. 2012 Damage caused by the American grey squirrel (Sciurus carolinensis) to agricultural
crops, poplar plantations and semi-natural woodland in Piedmont, Italy. Forestry, 80, 89-98.
[5]Wauters, L.A., Tosi, G., Gurnell, J., 2002. Interspecific competition in tree squirrels: do introduced gray squirrels (Sciurus carolinensis) deplete tree seeds hoarded by red squirrels (Sciurus vulgaris)? BEHAV ECOL SOCIOBIOL 51, 360-367.
Keywords: Decision Support Systems, Dominance-Based Rough Set Approach , GIS, GISMCDA Integration, Invasive Alien Species.
225
MO2D (contributed)
MCDM and GIS to Identify Land Suitability for
Agriculture
Abdelkader, Mendas (*)
CTS
The integration of MultiCriteria Decision Making approaches (MCDM) in a Geographical Information System (GIS) provides a powerful spatial decision support system which
offers the opportunity to efficiently produce suitability maps. Indeed, GIS is a powerful
tool for analyzing spatial data and establishing a process for decision support. Because
of their spatial aggregation functions, MCDM methods can facilitate decision making in
situations where several solutions are available, various criteria have to be taken into account and decision-makers are in conflict. A spatial decision support system has been
developed for establishing the land suitability map for agriculture. It incorporates the
multicriteria analysis method ELECTRE Tri (ELimitation Et Choix Traduisant la REalité) in a GIS within the GIS program package environment. The parameters and the
classification system used in this work are inspired from the FAO (Food and Agriculture
Organization) approach dedicated to a sustainable agriculture.The main purpose of this
research is to propose a conceptual and methodological framework for the combination
of GIS and multicriteria methods in a single coherent system that takes into account the
whole process from the acquisition of spatially referenced data to decision-making. In this
context, a spatial decision support system for developing land suitability maps for agriculture has been developed. The algorithm of ELECTRE Tri is incorporated into a GIS
environment and added to the other analysis functions of GIS. This approach has been
tested on an area in Algeria. A land suitability map for durum wheat has been produced.
Through the obtained results, it appears that ELECTRE Tri method, integrated into a
GIS, is better suited to the problem of land suitability for agriculture. The coherence of
the obtained maps confirms the system effectiveness.
Keywords: Assignment, Decision Analysis, Decision Support Systems, Multi-Criteria
Decision Analysis, Preference Aggregation.
226
TH4D (contributed)
MCDM in Nutritional Epidemiology
Gerdessen, Johanna (*)
Wageningen University
Banasik, Aleksander
Claassen, G.D.H. (Frits)
The intended conference presentation aims to make the audience familiar with an MCDM
challenge in nutritional epidemiology, an area so far hardly discovered by MCDM (and
vice versa). Epidemiological studies investigate the relationship between diet and disease.
Their relevance can hardly be overestimated, e.g. in designing effective and efficient intervention studies related to obesity, diabetes or cancer. For example, the hypothesis that
consumption of red and processed meat increases colorectal cancer risk while intake of
fish decreases risk is strongly supported by the results of an epidemiological study. Large
epidemiological studies commonly use food frequency questionnaires (FFQs) to assess a
population’s habitual (nutrient) intake. FFQs ask respondents about their consumption
of several food items during a predefined time period. On the one hand an FFQ should
include enough questions on food items to capture sufficient information on all nutrients
of interest. On the other hand an FFQ should be as short as possible, because long FFQs
are less cost and time efficient, may bore respondents and make them less motivated to
fill out an FFQ accurately. Selection of questions on food items to be included in an
FFQ is based on their contribution to the nutrient intake of a population. Aim of the
selection procedure is to compose a set of food items that captures as much information
as possible on a set of multiple nutrients. Each nutrient is represented with an objective
function, which makes it a MCDM problem. The aim of this presentation is to show
how MCDM can support the decision maker in the selection procedure. In consultation
with the program committee the presentation can focus either on a general form of the
model, or on the reformulation approach that was designed to solve a specific fractional
programming instance with more than 200 fractional terms.
Keywords: Healthcare, Linear Programming, Mixed Integer Programming.
227
WE2B (contributed)
Measuring Attractiveness of High School Programs
using Composite Index of Attractiveness and a
Multi-level Outranking Framework: Evidence from
PISA Survey in Serbia
Stamenkovic, Mladen (*)
University of Belgrade
Anic, Ivan
Backovic, Marko
Popovic, Zoran
In this paper we research attractiveness of high school programs in Serbia for students
with high and low level of competencies in three subjects: mathematics, reading and
science. We use PISA 2009 data. PISA survey is held every three years by OECD since
1997. We split students in four different groups by their socio-economic status (ESCS).
This status is given by ESCS index measured in PISA survey and conveys information
about social, economic, cultural and educational status of student’s family. As measure
of school attractiveness we create index of school attractiveness (ISA). We further investigate the attractiveness of high school programs by using ELECTRE MLO (ELECTRE
Multi-Level Outranking) approach, well used for outranking alternatives, based on one
modification of ELECTRE I method. The original ELECTRE I method is not good for
hierarchical comparison on set of alternatives because of cycles that can possibly occur.
In modification which we use in this paper cycles can be avoided by introducing the Absolute Significance Threshold. If the threshold value for the Concordance index is above the
Absolute Significance Threshold value, than there will be no cycles in the relation graph.
Moreover, the relation between alternatives is strictly ordered relation. Unlike ELECTRE
I where we have alternatives considered as in core or outside the core, ELECTRE MLO
calculates hierarchical levels of performance. The aim of the proposed ELECTRE MLO
approach is to produce a specific grouping of the selected high school programs, based on
the dominance relationships between them. We show that Gymnasiums have the most
attractive school program in Serbia. This means that the biggest part of high skilled students and the lowest number of low skilled students goes to this program. Furthermore,
the result we got using ISA and by ELECTRE MLO approach are in line and lead us to
the same hierarchical structure of high school programs.
Keywords: ELECTRE Methods, Outranking Methods, PISA Survey.
228
TU3C (contributed)
Measuring Performance of European Countries in the
Cultural Domain: an MCDM Approach
Nisel, Seyhan (*)
Istanbul University
Nisel, Rauf
Marmara University
Recent initiatives and studies have been carried out by the European Commission in the
field of culture have revealed the strategic importance of cultural aspects in sustainable
development. The European commission has generated wide variety of statistical data for
European countries in the field of culture, which provides the opportunity for extensive
analysis. The aim of this study is to propose a performance measurement model for EU-27,
EFTA and candidate countries in terms of cultural domain and practices. In the study, the
scope of the cultural field, evaluating criteria and data for countries were obtained from
cultural statistics that was published by Eurostat in 2011. The VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) methodology, applicable to many conflicting
and incommensurable criteria, was used for calculating the overall performance of the
countries. The research results showed the rank of the countries in terms of their performances in many different the cultural aspects.
Keywords: Culture, EU, Performance Measurement, VIKOR.
229
MO3B (contributed)
Microsoft Excel as a Tool for Solving Multicriteria
Decision Problems
Perzina, Radomir (*)
Silesian University
Ramik, Jaroslav
Silesian University
The Analytic Hierarchy Process is frequently used approach for solving decision making
problems. There exists wide range of software programs utilizing that approach. Main
disadvantage of those software products is that they are commercial and relatively expensive and thus it prevents them to be used by small companies, students or researchers.
This contribution introduces a Microsoft Excel add-in called DAME – Decision Analysis Module for Excel. Comparing to other computer programs DAME is free, can work
with scenarios or multiple decision makers, allows for easy manipulation with data and
utilizes capabilities of Microsoft Excel. Users can structure their decision models into
three levels – scenarios/users, criteria and variants. Items on all three levels can be evaluated either by weights or pair-wise comparisons. For each pair-wise comparison matrix
there is calculated an inconsistency index. There are provided three different methods
for the evaluation of the weights of criteria, the variants as well as the scenarios – Saaty’s
Method, Geometric Mean Method and Fuller’s Triangle Method. Multiplicative and additive syntheses are supported. All calculations are instant so users can easily see what
happen if anything is modified. Bar chart is used for final ordering representation. The
proposed software package will be demonstrated on couple of illustrating examples of real
life decision problems.
Keywords: Analytic Hierarchy Process, Microsoft Excel, Multi-Criteria Decision Making, Pairwise Comparison, Scenarios, Software.
230
TU5D (contributed)
Minmax Robustness for Multi-Objective Optimization
Problems
Ide, Jonas (*)
Ehrgott, Matthias
Schöbel, Anita
In real-world applications of optimization, the best solution is not always helpful if disturbances or other changes to the input data occur. Such changes in fact occur frequently
and can result in a non-optimality or even infeasibility of the optimal solution. One way
to deal with uncertain input data is robust optimization, the aim of which is to find
solutions which remain feasible and of good quality in all scenarios. For single-objective
optimization problems several definitions of robustness, i.e. when a solution is seen as
robust against such uncertainties, have been analyzed thoroughly. One of these concepts
is the concept of minmax robustness, where a solution is called robust if it is feasible
for every scenario and minimizes the objective function in the worst case. In this talk
we show that by solving a set-valued maximization problem, the concept of minmax robustness can be extended to multi-objective (MO) optimization and call this extension
minmax robust efficiency. We investigate properties of this concept, introduce strict and
weak minmax robust efficiency and discuss how minmax robust efficient solutions can be
computed. For the latter, we extend common techniques for calculating efficient solutions
for deterministic MO problems such as the weighted sum scalarization and the epsilonconstraint method. We investigate which theoretical results of the original techniques can
be adjusted to hold also in the uncertain setting and what relations between the techniques exist. As an extension we present other concepts of robustness which rely on other
definitions of the set-valued maximum. We conclude the talk by pointing out possible
areas of application such as portfolio optimization and supply chain management in the
renewable resource sector. For the latter we present an example from the veneer-cutting
industry where the cutoff and the storage volume have to be minimized with regard to
the uncertain quality of the used wood.
Keywords: Multi-Criteria Decision Making, Multi-Objective Optimization, Portfolio
Optimization, Robust Supply Chains, Robustness And Sensitivity Analysis, Scenarios.
231
TU3D (contributed)
Mixed Generation Sensitivity Analysis: An Optimal
Scenario Study
Wilson, Reginald (*)
Seattle City Light
There are several generating units available to meet the load demand on any given day.
The mixed-integer program case engages generating units that can be shut down or operated between minimum and maximum output levels within a specific time demand block.
An examination of these conditions espouses the utilization of renewable energy technology as a reliable power generation source as well as its effectiveness and augmentation
in a robust, heavily subjugated thermal-generation system. The mixed integer problem
has the predisposition to generate many sub-problems during the computation. However,
with careful selection of the parameters in the model, the challenges are easily overcome.
Hydroelectric energy is significant from an operational standpoint as it requires little
or no ramp-up time, commonly found in many combustion technologies. The essential
load-following capability, peaking capacity and voltage stability attributes renders hydroelectric energy as a significant part in ensuring reliable electricity service and in meeting
customer needs in a market driven industry. When this renewable resource is combined
with the thermal generation, an improved reliability function is apparent. Yet, in order to
minimize the environmental effects inherent in this type of energy resource, the renewable
power supply is expected to produce an effectual, consistent output. The integration of
renewable generation within the electrical system entails intricate analysis of the transmission line upgrades throughout the system. These upgrades are designed to relieve
congestion, improve reliability, and provide voltage support by thoroughly evaluating the
alternatives to accommodate the new expansion efforts. This paper examines the system
integration of renewable generators, via simulated studies, in the context of electrical operations from the hydroelectric generation perspective.
Keywords: Cost Avoidance, Mixed Integer Programming, Power Generation, Production Cost, Renewable Energy, Scenarios, Sensitivity Analysis, Smart Devices, System
Integration, Trade-Offs.
232
WE2E (contributed)
Mixed Integer Multi-Objective Optimization for Flight
and Maintenance Planning of Mission Aircraft
Gavranis, Andreas (*)
University of Thessaly
Kozanidis, George
University of Thessaly
Flight and Maintenance Planning (FMP) of mission aircraft addresses the question of
which available aircraft to fly and for how long, and which grounded aircraft to perform
maintenance operations on, in a group of aircraft that comprise a unit. The objective is
to achieve maximum fleet availability of the unit over a given planning horizon, while also
satisfying certain flight and maintenance requirements. Solution approaches that have
previously been developed to tackle this problem often perform unsatisfactorily, providing a long-term fleet availability with significant variability (defined as the sum of squares
of the deviations of the individual fleet availability values from their corresponding average
value). In order to address this issue, we introduce an additional objective that minimizes
the variability of the fleet availability, and we develop an exact solution algorithm for the
resulting multi-objective FMP model that is capable of identifying the entire frontier of
non-dominated solutions. The algorithm utilizes the discrete nature of the domain of
the fleet availability values, which is due to the fact that the fleet availability depends
solely on the number of different combinations of aircraft that can enter and exit the
maintenance station. Initially, the algorithm identifies an upper bound on the optimal
fleet availability by solving a simplified relaxation of the original problem; then this value
is gradually increased, and feasible aircraft combinations that minimize the variability
of the associated fleet availability are identified. Employing special valid inequalities for
excluding solutions that do not qualify for optimality, the algorithm obtains the entire
frontier of non-dominated solutions. The experimental results that we present demonstrate its high efficiency on randomly generated and realistic problem instances.
Keywords: Fleet Availability, Flight And Maintenance Planning, Mixed Integer MultiObjective Programming, Non-Dominated Frontier.
233
FR2D (contributed)
Modeling Consumer Preferences about Vehicles with
Multi-Attribute Additive Models: Survey-Based
Experiments
Dias, Luis (*)
University Coimbra
Oliveira, Gabriela
INESC Coimbra and University of Coimbra - MIT Portugal
Sarabando, Paula
IP Viseu - INESC Coimbra
This study concerns the choice of a vehicle focusing on its powertrain technology: gasoline, diesel, hybrid, plug-in hybrid or fully electric. The purchase of a vehicle is a standard
example of a problem that can be addressed by multi-criteria decision aiding (MCDA)
techniques, but most people do not conduct a multi-criteria analysis when facing that
choice. The study aims at finding out to what extent consumer preferences can be approximated by a multi-attribute additive model (additive value function). Surveys on
stated preferences were conducted by interviewers/analysts trained in this type of models. An initial survey (convenience sample, n=376) allowed defining which criteria should
be considered and provided lessons for the construction of a second survey. In this second
survey (n=252), subjects were also asked to answer a conjoint analysis type of questionnaire before and after performing MCDA. This presentation reports on some initial
conclusions derived from these experiments.
Keywords: Marketing, Multi-Attribute Value Theory, Preference Learning.
234
TU4D (contributed)
Multi-project scheduling as a MCDM problem
Krzeszowska, Bogumila (*)
University of Economics in Katowice
Trzaskalik, Tadeusz
One of the most important phase in project portfolio management is planning. During this
phase tasks are identified and scheduled. Schedule brings information how tasks should
be planned over time, during the realization phase of projects. In real-life applications
the schedule should optimize not only project finish time but also resource usage and cash
flows.
It shows that project scheduling is a MCDM problem. Anyway, there are only few papers
that consider the project scheduling in such a way.
We will consider multi-project scheduling. The purpose of this paper is to build mathematical model of the project portfolio scheduling problem and present it as a MCDM
problem. A discussion about decision variables, required constraints and optimization
criteria will be provided. Three criteria multi-project scheduling problem will be considered: sum of penalties for projects delay minimization, resource over-usage minimization
and NPV maximization.
Keywords: Project Scheduling, Multi-Project Scheduling, Multi-Criteria Decision Making.
235
MO3C (contributed)
Multi-Attribute Online Reverse Auctions
Wallenius, Hannele (*)
Aalto University
Long, Pham
National Economics University
Teich, Jeffrey
New Mexico State University
Wallenius, Jyrki
Aalto University
Online procurement auctions play an important role in e-Commerce. The basic principles
of such auctions originate from the concept of dynamic pricing and a bidding process
to set prices. In terms of B-to-B e-Commerce, online reverse auctions have been used
by many large corporations. However, concerns relating to online reverse auction usage
have been pointed out. One of the major concerns is that online reverse auctions only
concentrate on the interests of the buyer, while ignoring the interests of the suppliers. It
is true that long-term relationships between buyer and supplier can be damaged, if price
is the only priority of the buyer. In order to overcome the criticism, new auction mechanisms that take non-price attributes, such as quality, delivery and payment terms into
consideration have been suggested. Several commercial software vendors provide platforms for conducting such auctions known as Multi-Attribute Online Reverse Auctions
(MAORAs). Increasing attention is being paid to MAORAs because of their ability to
enable buyers to thoroughly compare the relative strength of potential suppliers based
on multiple attributes, including but not limited to price. Theoretically, MAORAs are
expected to lead to competitive bidding and allocative efficiency. However, contrary to
a common belief, online reverse auctions can generate benefits not only for buyers but
also suppliers. Via online reverse auctions, suppliers can gain market information, create
new markets for better excess capacity management, and attract new customers. The
objective of this paper is to gain a deeper understanding of MAORAs, how to implement
them and their outcomes. This is achieved through an extensive state-of-the-art literature review of articles published in academic journals. Specific issues related to MAORAs,
such as rules, choice of attributes, reservation prices, the role of bid decrement, scoring
functions, winner determination, and information architecture are discussed.
Keywords: Auctions/Bidding, Multi-Attribute, Reverse Auctions.
236
FR3F (contributed)
Multi-Criteria Decision Analysis for Water Supply
Infrastructure Planning under Uncertainty
Scholten, Lisa (*)
Eawag
Lienert, Judit
Eawag
With asset lifetimes ranging from a few decades to more than a century, water supply
infrastructure choices are to be made on the long-run. To support long-term planning of
water supply systems, multi-attribute utility theory (MAUT) under four future scenarios
is applied to a case study in Switzerland. The problem-framing was done in workshops
and individual interviews in which stakeholders from different professional backgrounds
representing the local, cantonal, and national levels were involved. Altogether, eleven decision alternatives -based on organizational, managerial and technical characteristics- are
evaluated under five fundamental objectives, which in turn consist of 30 lower level fundamental objectives. Different sources of uncertainty are often ignored in practical MCDA
due to the complexity of elicitation and preference modeling. Several studies addressing these uncertainties exist. Nonetheless, the relative importance of single uncertainty
sources for overall uncertainty, and thus the ability to discriminate between alternatives,
is usually not investigated. Instead of using the common simplifications (equal weights,
linear value functions, risk neutrality, additive aggregation) and ignoring potentially important uncertainties, we 1) elicit weights, value functions, and risk attitude in interval
form from ten stakeholders using a simplified procedure, 2) quantify the alternative’s outcomes with regard to the attributes including uncertainty using model predictions and
expert judgment, and 3) use this information for a global sensitivity analysis to find out
which of the individual uncertainty sources contribute most to overall uncertainty. This
will help to focus on more adapted elicitation design and methodic improvements which
better account for, and reduce, uncertainty in complex decision support situations.
Keywords: Decision Analysis, Multi-Attribute Utility Theory, Multi-Criteria Decision
Analysis, Sensitivity Analysis, Uncertainty Modelling, Water Infrastructure Planning,
Water Resources Management.
237
MO3B (contributed)
Multi-Criteria Decision Making Applications in Higher
Open and Distance Learning Systems
Kamisli Ozturk, Zehra (*)
Anadolu University
In this study, we consider a number of the general multi criteria decision problems that
may be encounter in many Higher Open and Distance Learning (ODL) institutions, and
we also discuss possible solution approaches like mathematical programming approaches,
AHP, ANP and genetic algorithms. The terms open and distance learning represent approaches that focus on opening access to education and training provision, freeing learners
from the constraints of time and place, and offering flexible learning opportunities to individuals and groups of learners. The management of ODL institutions often differs from
the management of traditional face-to-face educational institutions, this being reflected in
the structure of the institution and generating a clear differentiation between the types of
learners. The management of ODL institutions has a particular character reflecting the
wide area served by the education provision. Some of the multi criteria problems faced
in ODL systems are vehicle routing, learner-practice assignment, application model selection and learning management system selection. As seen from the examples, managers
have to make decision around various tasks continuously. Usually, the effort is towards
minimising the operating costs of ODL systems and as technological progress continues
these multi objective decision problems will continue to occur. The presence of multiple
actors, e.g. academic personnel and learners etc., brings out user satisfaction problems
and issues that complicate these decision problems and, furthermore, in educational institutions most of the problems are institution-specific because of particular local constraints
and parameters. Under these circumstances, in the solution processes of most problems
a need for heuristics occurs. We have looked at a small subset of ODL related decision
problems in this study, and many of the problems discussed here can be found across the
spectrum of traditional higher education systems.
Keywords: Analytic Hierarchy Process, Analytic Network Process, Assignment, Genetic
Algorithms, Open And Distance Learning.
238
TU3B (contributed)
Multi-Criteria Decision Making Approach for Resource
Efficient Bread Supply Chains
Banasik, Aleksander (*)
Kanellopoulos, Argyris
van der Vorst, Jack
Resources are becoming scarce, ecosystems are threatened and people become increasingly aware of the environmental pressure caused by food production. With this changing
socio-economic environment, the bread industry in the Netherlands needs to adapt to
remain competitive too. Therefore all links of the supply chain pursue for solutions that
result in sustainable production and efficient use of resources. The current structure of
the Dutch bread industry in relation with the nature of the product (i.e. short self-life)
results in large waste streams. It is estimated that in the Netherlands, one third of the
total bread production is lost as waste. This research focuses on improving sustainability
performance, resource efficiency and effectiveness of the whole chain such that raw materials and resources are used to their full potential. To achieve this, existing inefficiencies
should be identified, quantified and eliminated, and opportunities for valorization must
be explored. Looking to the chain as a whole enables a more holistic analysis but also
increases complexity because of the conflicting objectives in different links of the chain.
Moreover, the multiple and to a large degree conflicting dimensions of sustainability (i.e.
economic, environmental and social) add to the complexity involved in quantifying and
analyzing performance of the bread production chain. In this study, exergy, which is
proposed in engineering as a comprehensive indicator of the environmental dimension
of sustainability, and advanced quantitative decision support tools is used to optimize
production and re-design the logistical structure of the whole chain. A Multi Criteria
Decision Making model is proposed to support managerial decisions and improve sustainability performance of the bread food supply chain in the Netherlands. Pareto-optimal
solutions and trade-offs between a number of economic and environmental indicators are
calculated.
Keywords: Exergy, Food Supply Chains, Sustainability.
239
MO4F (invited)
Multi-Criteria Decision Making: Exergy and Energy
Life Cycle Analyses for Plastic Individual Disposable
Bags versus other Material
Escobar-Toledo, Carlos Enrique (*)
National University of Mexico
Alegr´ıa Mej´ıa, Lol-chen
University of Mexico
Ram´ırez, Bárbara
This paper explores the application of a multi-criteria decision making model within energy utilization in sustainable development and the potential sources to increase energy
efficiency in the manufacture of plastic materials used for disposable bags as well as other
material used for the substitution of plastic bags. The Exergy loss during the whole
life cycle, which lead to the analysis of production routes with better thermodynamic
performance and hence less damage to the environment, is presented as a tool in aid of
the trade-off of alternative materials in the multi-criteria decision making. Because the
comparison between materials and / or production processes is more complete when you
look at more than one criteria, are also analyzed other factors besides the irreversibility as: the amount of fossil fuels consumed (expressed as barrels of oil equivalent), the
amount of energy consumption (also expressed in barrels of oil equivalent), the amount
of emissions of greenhouse gases and the product value as an economic factor. The material analyzed are: polyethylenes of high and low density (HDPE and LDPE), the same
with an additive to induce degradation, Kraft-type paper, cotton and polypropylene (PP).
Keywords: Decision Making, Energy System, Exergy, Life Cycle Analysis, Multi-Criteria
Decision Analysis.
240
WE1D (invited)
Multi-Criteria Decision Support for Designing Critical
Infrastructure Stress Tests
Comes, Tina (*)
University of Agder
Bertsch, Valentin
Critical Infrastructures (CIs), such as the energy system, are usually defined by their
importance for societies and economy, or by the consequences of their disruptions. Still,
there is no unanimous definition of what actually are the most severe consequence that
must be avoided. Recent events such as Hurricane Sandy illustrated the lack of preparedness and the need for more robust CIs, particularly with respect to the energy system.
As modern societies are characterised by complexity and interlacedness, the impact of
failures of CIs cannot be contained regionally or among a small group of stakeholders.
Therefore, the need arises for participatory approaches ensuring transparency and facilitating consensus-building. Stress tests for financial or engineered systems have been
created to protect population and economy against the consequences of the failure of highconsequence systems. The design of these stress tests typically starts with constructing
events (stress test scenarios) that are a means to disturb the system. Usually, stress test
scenarios do not cover emerging and unprecedented risk, and there is no systematic way to
steer and manage the scenario construction such that it reveals the most critical elements
of the system. Criticality is understood in terms of vulnerability (susceptibility to failure)
and consequence or harm. We will present an MCDA approach to design stress tests for
CIs. For its outstanding importance, we will focus on the energy. This approach focuses
on requirements for stress tests, such as protection levels desired or minimum plausibility
of stress test scenarios, but also takes into account longer-term goals of economic growth
and well-being. In this way, decision-makers are supported in balancing efficiency and
effectiveness, i.e., in making trade-offs between optimisation for standard operations and
foreseen changes vs. ensuring that service levels can be achieved even when the system is
stressed by shocks or unexpected trends.
Keywords: Energy System, Participatory Evaluation, Risk Management, Scenarios, Vulnerability.
241
TU3D (contributed)
Multi-Criteria Decision Support in Paper Production
Industry
Claassen, G.D.H. (Frits) (*)
Gerdessen, Johanna
Pulp and paper industry is an extremely large business characterised huge capital costs.
As margins are low, a continuous search for efficient production and decision support is
of utmost importance in this branch of industry. Faced with rising raw material and energy costs, pulp and paper producers have to optimise the performance of their processes
to remain competitive. Reducing costs, e.g. for raw materials, is an important way to
increase or (at least) maintain the annual operating profit. We present the development
and continuous improvements of an OR-based Decision Support System (DSS) in paper
production industry. The first pilot system was handed over to the company in 1990.
After two decades the basic concepts of the initial system are still used on a regular basis. However, pressed by changed circumstances, i.e. the acquisition by a large globally
operating company with significantly more mills and the constantly advancing physical
knowledge of large scale pulp- and paper production, management decided to upgrade the
system to a flexible tool for global decision support on different decision levels within the
company. Several features are added, updated and improved. One of the main concerns
from practice was that a single overriding objective (i.e. minimizing costs) is too restrictive for future decision support. Nowadays, management assistants focus on a variety of
objectives. The aim for a DSS in an MCDM context did have a substantial impact on
the latest upgrade in which mixed integer (0-1) fractional programming was introduced.
The aim of this contribution is 1) to present the main characteristics of the DSS, 2) to
show why an MCDM approach induced a (0-1) fractional programming problem and 3) to
demonstrate how various, partly non-linear, and conflicting objectives can be solved such
that the added value of the system is recognized and adopted by management assistants
in practice.
Keywords: Applications of MCDM, Decision Support Systems, Fractional Programming, Mixed Integer Programming, Multi-Criteria Decision Making.
242
MO4G (contributed)
Multi-Criteria Decision-Making Perspective on the
Privacy versus Security Conundrum
Toubaline, Sonia (*)
Borrion, Hervé
Le Sage, Tanya
Mitchener-Nissen, Timothy
In the last decade, many governments have responded to evolving criminal threats by
funding and implementing new security policies and technologies. Whilst many of these
constitute enhanced versions of existing measures, a number of them were specially designed to accompany emerging technologies and applications such as social networking.
As the threat of terrorism and cybercrime continue to rise in our democratic societies,
a major challenge for those in charge of developing effective security measures lies in
minimizing the ethical issues associated with their implementation. Faced with the possibility of moving towards the ‘surveillance society’, experts and non-experts recurrently
take part in debates organised to assess the need for new security measures. However,
typical presentation of the problem which often consists of opposing privacy and security
is sometimes criticized for inherently supporting securitization of our society. In this article, we analyse the privacy-security conundrum using a multi-criteria decision making
framework. We discuss some of the limitations arising from framing the problem as a
privacy-security tension, and provide several directions that may be used to better guide
and inform the public and policy-makers on matters related to security. The paper concludes by presenting some initial results obtained from an attempt to elicit a clear list of
criteria for the specific purpose of assessing security measures.
Keywords: Applications of MCDM, Criteria, Decision Making, Privacy, Risk Management.
243
TU5E (contributed)
Multi-Criteria IMRT Optimisation based on the
Generalised EUD Model
Cabrera G., Guillermo (*)
Ehrgott, Matthias
Mason, Andrew
Intensity modulated radiation therapy is one of the major forms of cancer treatment. Its
main goal is to eradicate all clonogenic cells by delivering ionizing radiation from an external source to the tumour without compromising surrounding normal tissues. To design
a treatment plan firstly the number and directions of radiation beams are determined.
Then, the intensity of the radiation for each beam angle needs to be computed. Finally,
radiation is modulated using a physical device called a multileaf collimator which requires
the solution of a sequencing problem. In this talk we address the second problem, known
as the fluence map optimisation (FMO) problem. FMO attempts to find a combination of
beam intensities that produce a clinically acceptable treatment plan. Several models have
been proposed to solve the FMO problem. A well-known measure of the biological effects
of the delivered radiation dose is the generalised equivalent uniform dose (gEUD). One
advantage of gEUD-based optimisation is that it penalises hot and cold spots in organs
at risk (OAR) and target regions, respectively. Therefore, gEUD allows us to evaluate
and compare different intensity maps generated during the solution of the FMO problem even though these intensity maps produce non-uniform dose distributions. Based on
the fact that gEUD is a positively homogeneous function, we propose a new strategy to
solve a multi-objective FMO model. This model attempts to maximise the target (tumour) gEUD while minimising the gEUD of the OARs. Numerical experiments applied
to a prostate cancer case with three structures, namely, prostate, bladder and rectum
have been carried out. Results show that, based on our proposal, the whole efficient set
can be found by following a simple procedure which includes the solution of a convex
single-objective optimisation problem which can be solved to optimality using well-known
gradient based algorithms.
Keywords: Intensity Modulated Radiation Therapy, Multi-Objective Optimization, Positively Homogeneous Functions.
244
TH3C (contributed)
Multi-Criteria Preference Disaggregation Analysis for
Classification: an Application to Model Colour
Preferences
Ghaderi, Mohammad (*) Agell, N´
uria
ESADE, Universitat Ramon Llull
Ruiz, Francisco Javier Sanchez, Monica
Classification or discrimination problems consider the assignment of a set of alternatives
into predefined groups. In some situations, groups are defined in an ordinal way from
the most to the least preferred. In the multi-criteria decision-aid (MCDA) literature,
this is known as a ‘sorting’ or ‘learning preferences’ problem. Capturing the decision
makers (DMs) tacit knowledge, by providing them a training sample to be sorted in an
ordinal way, is considered of interest in the knowledge management field. Extracting and
mathematically framing the preference system of the decision maker (expert) enables us
to predict preferences for cases that are outside of the training sample. Much effort has
been made in this direction in the area of artificial intelligence, specifically in fuzzy set
theory and machine learning systems. Preference disaggregation, as one of the most popular approaches for capturing the preference system of DMs, in MCDA is used to infer
global preference models from given preferential patterns. Among others, we can highlight: UTA (UTilites Additives); UTASTAR; UTADIS (UTilites Additives Discriminates)
; ELECTRE TRI ; and MHDIS methods. The aim of these approaches is to provide a
model that is as consistent as possible with the decisions made by the DM. This research
includes a literature review of the existing methodologies for learning preferences and a
comparison between some of them. An application related to colour preferences is used to
compare these methodologies. Finally, managerial applications involving learning colour
preferences are studied.
Keywords: Aggregation Schemes, Classification, Decision Maker’s Preferences, Fuzzy
Logic, Multi-Criteria Decision Analysis, Preference Disaggregation, Preference Learning.
245
FR3E (contributed)
Multi-Criteria Sorting with Category Size Restrictions
¨
Ozpeynirci,
Selin (*)
˙
Izmir
University of Economics
Koksalan, Murat
Mousseau, Vincent
In this study, we consider the multi-criteria sorting problem where alternatives that are
evaluated on multiple criteria are assigned into ordered categories. Sorting problems arise
in many real life situations such as classifying countries into different risk levels based on
economical and sociopolitical criteria, evaluating credit applications of bank customers,
evaluating student applications for graduate programs and selecting projects for different
kinds of funding policies. There are several approaches to the sorting problem in the literature based on estimating a utility function, ordinal classification, outranking relations,
and interactive approaches. We focus on the sorting problem with category size restrictions, where the decision maker may have some concerns or constraints on the number of
alternatives that should be assigned to some of the categories. We develop an approach
based on the UTADIS method that fits an additive utility function to represent the decision maker’s preferences. We introduce additional variables and constraints to enforce the
restrictions on the sizes of categories. We demonstrate our approach on two applications
from practice.
Keywords: Category Size, Multi-Criteria Sorting, UTADIS.
246
WE2F (invited)
Multi-Criteria Timetable Information - Extending the
Pareto Approach
Schnee, Mathias (*)
Technische Universitat Darmstadt
Keyhani, Mohammad Hossein
Technische Universitat Darmstadt
Timetable information systems calculate train connections and customers choose among
these according to personal preferences. This is a multi-criteria decision in a natural
way with profiles varying drastically between customers, e.g. compare a business traveler
primarily concerned with travel time to an elderly person focusing on the number of interchanges and ticket cost. To satisfy all types of customers, we developed a timetable
information system (MOTIS) based on multi-criteria optimization. An engineered generalization of a Pareto version of Dijkstra’s algorithm on a suitably constructed timeexpanded timetable graph is used to determine attractive connections for all users. The
developed models and algorithms are fully realistic and are applied on real data from
German Railways. Besides the classical criteria travel time, number of train changes, and
ticket cost, we also regard the difference in departure and arrival times, uninterrupted
sleeping time in night trains, special offers based on contingents for trains, and the reliability of train changes. This reliability is based on probability distributions for the
delays of arrival and departure events. In order to determine all attractive connections,
we extended the classical Pareto-approach to a) find additional connections (e.g. only
slightly worse but largely differing departure and arrival times) and b) disregard undesired Pareto-optima (e.g. much longer travel time but only slightly less expensive). The
system is efficient (less than 2 seconds on average for real customer queries) and finds a set
of high quality connections with reasonable size (4-8 optima on average). In this paper,
we will formally introduce our extended Pareto-approach and discuss the treatment of the
different criteria. We will evaluate the repercussions of the chosen optimization approach
(classical Pareto or our different extensions) and will present a study on the optima and
run time depending on the selection of criteria.
Keywords: Multi-Objective Optimization, Shortest Path, Timetable Information, Train
Networks.
247
MO4E (contributed)
Multi-Objective Dynamic Scheduling Optimization
Model for Real-Time Rebalancing Control of the
Hybrid Assembly Lines
Wong, Wai Keung (*)
Hong Kong Polytechnic University
This paper investigates the dynamic scheduling optimization problem (DSOP) of hybrid assembly lines in apparel manufacturing. A reliable, flexible and effective dynamic
scheduling system was proposed for rebalancing control of real-time hybrid assembly lines.
There are three stages in our proposed approach. First, the updated radio frequency
identification-based (RFID) data capture system is adopted to collect real-time production records, which provides a guarantee for rebalancing control to react to disturbances of
production conditions accurately and timely. Second, a discrete event simulation model
is developed to serve as dynamics of hybrid assembly lines, which solves the difficulty
of calculating objective functions when no closed-form mathematical formulation exists.
The third stage is to establish a dynamic scheduling optimization model for rebalancing
control of hybrid assembly lines. An enhanced discrete differential evolution (EDDE) algorithm was proposed to find an optimal solution to the DSOP. The EDDE algorithm was
enhanced by integrating heuristic selection strategies to yield a satisfactory solution as
quickly as possible and repairing strategies to handle constraints effectively. The results
from the numerical experiments verified the feasibility and effectiveness of the proposed
EDDE algorithm in finding optimal solutions to the DSOP. Compared with industrial
practice, our proposed system can improve production efficiency with an increment of
above 17%, which leads to reduction in production time of about 15% for orders of the
same size, which demonstrates that the proposed dynamic scheduling system is a promising approach to improvement in efficiency and productivity of hybrid assembly lines by
effective rebalancing control. After successful validation, the proposed model is now practically used by the manufacturers in the apparel industry in Mainland China.
Keywords: Line Balancing Control, Multi-Objective Optimization, Operator Allocation, Production.
248
TH3E (invited)
Multi-Objective Framework for Model Based Design of
Experiments: Application to Hemodialysis and Type 1
Diabetes Model
Maheshwari, Vaibhav (*) Rangaiah, Gade Pandu
Samavedham, Lakshminarayanan
National University of Singapore
Model-based design of experiments (MBDoE) has grown into an indispensable tool for improving parameter precision of dynamic models. In the last two decades, the applications
of MBDoE have extended from chemical kinetics to biomedical applications, biological
networks, biological processes (fermentation and bio-kinetics), environmental engineering, etc. Traditionally, alphabetical design D-, A-, and E-design criteria are used for
model-based experimental design, where each design maximizes the information measure
for improving precision of parameters. The information measure is calculated from Fisher
information matrix (FIM). However, it is observed that maximizing the information measure, inevitably leads to increase in correlation among parameters. This increased correlation is associated with poor point estimates and/or reduced precision of parameters.
To overcome the weakness of traditional alphabetical designs, a multi-objective optimization (MOO) based framework for experimental design has been proposed. The proposed
framework consists of two contradicting objectives, namely, (1) maximize the information
measure (as in traditional alphabetical design), and (2) minimize the correlation among
parameters. In this work, we illustrate the application of proposed experimental design
framework for a toxin kinetic model in hemodialysis and Type 1 diabetes model, and
compare its performance with alphabetical designs. For each of D-, A-, and E-designs,
corresponding DMOO, AMOO, and EMOO is proposed and its performance is assessed
for parameter precision and correlation. Obtained results indicate that MOO based experimental design framework provides improved point-estimates when compared with those
obtained from alphabetical design. Acknowledgement: We thank National Kidney Foundation, Singapore for supporting this research through NKFRC/2011/01/06.
Keywords: Correlation, Model-Based Design Of Experiments, Multi-Objective Optimization, Parameter Precision, Point Estimates.
249
MO4B (invited)
Multi-Objective Modelling and Simulation in New
Product Development (NPD) using the Belief
Rule-Based (BRB) Methodology
Savan, Emanuel (*)
Chen, Yu-Wang
Xu, Ling
Yang, Jian-Bo
In this paper, the novel belief rule-based (BRB) methodology is employed to identify
the underlying relationships between instrumental measurements and their corresponding sensory attributes. The developed BRB model is then used to support the decision
making (DM) process in new product development. As a case study, data collected from
Unilever on consumer perception of 70 products over 55 sensory attributes has been used
along with over 100 instrumental measurements. In the modelling phase, one of the most
prominent interests raised by the practitioners concerned the model’s validation and overfitting. Given the recent development of the BRB methodology, this model building phase
has not yet been formally addressed. Consequently, one of the main purposes of this paper
is to develop a validation framework capable of indicating the appropriate model structure. In order to guarantee the comprehensiveness of the carried validation, the proposed
framework was built based on three complementary criteria. The Akaike information
criterion is used in conjunction with another well recognised approach: the Bayesian information criterion, and with a heuristic approach, in order to identify the most suitable
model structure. Various possible structures are generated following a compilation of
search heuristics: genetic algorithms, tabu search and a heuristic expert-knowledge based
search. The purpose of this research is twofold. On one hand, the methodology is applied
to a new, complex, DM context. It is compared with Unilever’s initial model, as well
as with the most popular methodologies in the field: artificial neural networks, support
vector machines, and multiple linear regression. It is interesting to note that the BRB
methodology outperforms the other approaches in the case study, proving highly suitable
for the addressed DM context. On the other hand, a validation framework is developed
and analysed. The proposed model structure proved highly robust upon testing.
Keywords: Belief Rule-Based methodology, Model Testing And Validation, New Product Development, Over-Fitting, Sensory Attributes Prediction.
250
TU2G (invited)
Multi-Objective Optimisation for the Modeling and
Control of Uncertain Dynamic (Bio)Chemical
Processes
Logist, Filip (*) Coenen, Thomas Stamati, Ioanna
Telen, Dries Vallerio, Mattia Van Impe, Jan
Katholieke Universiteit Leuven
Many (bio)chemical processes are dynamic in nature and their optimisation involves the
determination of optimal time-varying control policies. Moreover, in view of sustainable
development multiple objectives arise. For instance not only more profitable (economically sustainable), but also cleaner (environmentally sustainable) and safer processes
(socially sustainable) processes are aimed at. To enhance decision making in practice,
it is important to enable the efficient generation of optimal alternatives (Pareto optimal
solutions) and the evaluation of the trade-offs between them. Recently a freely available
toolkit ACADO (www.acadotoolkit.org) for multi-objective optimal control of dynamic
systems has been introduced. The rationale behind the approaches is a tight integration of
efficient multi-objective scalarisation strategies (e.g., Normal Boundary Intersection and
Normalised Normal Constraint) with fast deterministic dynamic optimisation approaches
(e.g., Single and Multiple Shooting). Some recent developments will be illustrated for different (bio)chemical applications. First, results are presented for the multi-objective optimisation under uncertainty of fed-batch fermentors for the production of lysine. Second,
multi-objective optimisation is applied to the experimental design of dynamic experiments
for modeling the temperature dependency of the growth rate of micro-organisms. Third,
a case study is discussed regarding the tuning of nonlinear model predictive controllers
in view of grade changes in a large-scale chemical plant. For each of the three cases the
Pareto set as well as the corresponding optimal operation policies are presented.
Keywords: Applications of MCDM, Chemmical Engineering, Control, Dynamic Optimization, Dynamic Systems, Multi-Objective Optimization, Optimal Control.
251
FR3D (contributed)
Multi-Objective Optimization of Combinatorial
Problems with Fuzzy Data
Bahri, Oumayma (*)
ISG Tunis - University of Tunis
Ben Amor, Nahla
ISG Tunis
El-Ghazali, Talbi
University of Lille
Optimization under uncertainty is an important line of research, having today many
successful real applications in different areas. Despite its importance, few works on multiobjective optimization under uncertainty exists today. However, the panoply of existing
studies has been often experimented on deterministic multi-objective problems or even
on uncertain multi-objective case but by treating it as mono-objective (in considering
the set of objectives as if there is only one). The aim of our study is to deal with such
a multi-objective problem with uncertain data, using the possibility theory which offers
a flexible and efficient approach for handling uncertainty. So, we propose firstly a new
adaptation of the possibilistic framework, in which the uncertain data are represented by
a specific form of possibility distributions, commonly known as triangular fuzzy numbers.
Then, knowing that the main goal of solving a multi-objective problem is to identify the
set of optimal solutions called ’Pareto optimal set’, the result obtained in our case will
not be a set of exact solutions but a set of uncertain solutions disrupted by the used fuzzy
form. Clearly, the obtained fuzzy solutions cannot be classified by using the classical
Pareto dominance. Thus, we propose at the second stage a new Pareto dominance for
ranking triangular fuzzy numbers. The proposed method is then applied to solve a multiobjective variant of Vehicle Routing Problem (VRP) with uncertain customer demands.
Finally, to validate our study, two well known multi-objective algorithms: PLS1 (Pareto
Local Search Algorithm) and SEEA (Simple Elitist Evolutionary Algorithm) are adopted
and then developed under the software platform dedicated to multi-objective optimization: ParadisEO-MOEO. The both algorithms are tested on a few instances of Solomon’s
benchmark and evaluated by using two quality metrics: Hypervolume and Epsilon indicators.
Keywords: Multi-Objective Optimization, ParadisEO-MOEO, Pareto Dominance , Pareto Local Search Algorithm, Possibilistic Framework, Possibility Theory, Simple Elitist
Evolutionary Algorithm, Triangular Fuzzy Numbers, Uncertainty, Vehicle Routing Problem.
252
TU2G (invited)
Multi-Objective Optimization of Integrated Design
and Control of a Paper Mill
Steponavice, Ingrida (*)
Miettinen, Kaisa
Many real-life engineering design problems involve an extensive use of dynamic process
simulations. Engineers rely on these simulations to develop new products, better understand experimental results and build confidence in solutions they develop. Solving dynamic problems is very demanding due to typically high computational cost, uncertainty
and intractability related to dynamic process simulation. Moreover, it is often insufficient
to only consider a single objective function to be optimized. Instead, many optimal design
problems have multiple conflicting objective functions that must be optimized simultaneously. Multiobjective optimization problems have multiple Pareto optimal solutions
which are mathematically equal and a human decision maker is necessary to determine
the most desirable one. With an increasing complexity of design problems, finding an
optimal design in real-life applications remains a challenging task. We present an efficient
approach to search for optimal solutions of such problems enabling an effective decision
making process allowing learning and exploring different trade-off solutions. We demonstrate our approach with a case study in paper making industry. To be more specific, we
consider both optimal design and control of a paper mill, where we aim at decreasing the
investment cost and enhancing the quality of paper on the design level and, at the same
time, guaranteeing the smooth performance of the production system on the operational
level.
Keywords: Applications of MCDM, Computational Cost, Decision Making, Pareto Optimal Solutions.
253
MO5H (poster)
Multi-Objective Optimization Problems in Statistical
Machine Translation
Duh, Kevin (*)
Nara Institute of Science and Technology
Sankaran, Baskaran
Simon Fraser University
Sarkar, Anoop
Simon Fraser University
Machine Translation has been one of the holy grails of Artificial Intelligence, the goal
being to convert a sentence in one language to another language while preserving the
meaning. Thanks to the increasing availability of large textual datasets and the increasing performance of statistical optimization algorithms, the field has advanced significantly
in the past decade. Many commercial systems, such as those from Microsoft and Google,
now employ the statistical approach when developing the translation system. Often, these
systems are built by single-objective optimization methods. The objective is the quality
of the translation, as defined by some type of string matching metric between the system’s
translation and a human translator’s sentence. However, translation quality is a slippery
concept and many types of matching metrics based on different linguistic insights have
been proposed. Thus we argue that the best approach is to optimize the system on these
multiple objectives concurrently. In this work, we describe a formulation of the multiobjective optimization problem in Statistical Machine Translation. We will also discuss a
simple solution based on incorporating Pareto Frontier ideas into a Support Vector Machine classifier. The purpose of this presentation is to introduce this novel application
of multi-objective optimization to the MCDM community; we hope to learn about some
new solution techniques in return.
Keywords: Applications of MCDM, Artificial Intelligence.
254
MO2F (invited)
Multi-Objective Reinforcement Learning using Sets of
Pareto Dominating Policies
Van Moffaert, Kristof (*)
Vrije Univesiteit Brussel
Drugan, Madalina M.
Nowé, Ann
Many real-world problems involve the optimization of multiple, possible conflicting, objectives. Multi-objective reinforcement learning (MORL) is an extension to standard reinforcement learning where the scalar reward signal is extended to multiple feedback signals,
i.e. one for each objective. Thus, MORL is the process of learning policies that optimize
multiple criteria at the same time. We aim to combine multi-criteria decision making
(MCDM) and evolutionary multiobjective optimization (EMO) techniques to construct
hybrid MORL algorithms. In this paper, we present a novel algorithm that integrates the
Pareto dominance relationship into a reinforcement learning approach. This algorithm is
called Pareto Q-learning and it is an extension to the single-objective Q-learning algorithm
that keeps track of sets of Pareto dominating policies. We also propose two mechanisms
for using Pareto Q-learning in an on-line setting, i.e. when the cost of sampling actions is
crucial and the agent gradually improves its performance. More precisely, these mechanisms use the hypervolume measure and Pareto dominance relationship to select the most
promising actions. We test the algorithm on multiple environments with two and three
objectives. We demonstrate that Pareto Q-learning outperforms current state-of-the-art
MORL algorithms and is able to find the entire Pareto front on two benchmark instances.
Keywords: Multi-Criteria Decision Analysis, Multi-Objective Optimization, Pareto Set,
Reinforcement Learning.
255
TU2D (invited)
Multi-Objective Restricted Dynamic Vehicle Routing
Problem with Time Windows
De Armas, Jésica (*)
Universidad de La Laguna
Melián, Belén
Univesidad de La Laguna
Moreno Pérez, José A.
Universidad de La Laguna
The Dynamic Vehicle Routing Problem with Time Windows (DVRPTW) consists not
only in finding a set of optimal routes for some vehicles in order to serve a set of customers with specific time windows, but also in readjusting the vehicle routes to add new
customers that appear in real time. We tackle a real-world application of this problem
proposed by a delivery company, in which the vehicles are loaded at the beginning of
the planning horizon and then the arising services are inserted into a route supposing
the minimum infeasibility. The company knows all the deliveries, but only the 60% of
the pick-ups. The remaining 40% is dynamic since it appears during the day. Moreover,
the following constraints have to be considered. The vehicles have different capacities;
each of them can have a different time window, as well as the customers; extra hours for
the vehicles might be allowed; the customers have a level of priority so that they can be
postponed if they have less level of priority than a fixed one; and finally, some customers
cannot be served by certain vehicles due to road restrictions. Besides, the problem is
zoned; a problem supposes the solution of different sub-problems. The objective functions have been taken into account in lexicographic order, in such a way that if there
is a draw between two different plans for one objective, the next one is compared. The
order used in this work is: number of postponed customers, extra time used by the vehicles, total traveled distance, number of routes, total consumption and total cost. The
computational experiments have been carried out by testing the approach over the test
problem instances in the related literature. Thus, we have obtained competitive results
if we compare them with the best known results. Furthermore, we have generated and
tested instances specifying the constraints of the real application. Finally, nowadays we
are doing real tests using the 4GFlota system, which allows managing a fleet of vehicles.
Keywords: Dynamic VRPTW, Multi-Objective Optimization.
256
TH4F (contributed)
Multi-Objective Task Allocation using Multi-Agent
Coalition Formation
Ben Abdelaziz, Fouad (*)
Rouen Business School
Amer, Noha
American University of Sharjah
In this paper, we address the multi-objective task allocation problem. We consider a set
of agents each with a vector of limited and/or negative resources that shall be cooperating
in order to perform a set of tasks. The cooperation leads to a numerous number of coalitions. In addition, We consider two objectives; maximizing the efficiency of the system
and maximizing the agent’s payoff. These two objectives represent two different types of
agent’s behaviors; cooperative agents and selfish agents. To solve this problem, we design
a genetic algorithm and we compare the obtained results with those obtained by an exact
algorithm for small sized problems. We also address the hybrid behavior where agents
aim to maximize the efficiency of the system and their own payoff at the same time. We
also conclude about the influence of agents’ behavior regarding the performance of task
allocation.
Keywords: Coalition Formation, Genetic Algorithms, Multi-Objective Optimization,
Task Allocation.
257
TU4E (contributed)
Multiattribute Utility Analysis using Strict Preference
Relations Elicited from a Decision Maker
Ichiro, Nishizaki (*)
Masakazu, Ohmi
Tomohiro, Hayashida
To identify multiattribute utility functions with the mutual utility independence, decision makers must specify indifference points and subjective probabilities precisely. By
relaxing such exact evaluation, multiattribute value models with incomplete information
have been developed. In this paper, we present a new method finding a best alternative
through strict preference relations derived from the decision makers by questions that are
not difficult to answer. For given strict preference relations, in our method alternatives
satisfying the relations can be obtained by solving a mathematical programming problem
with constraints representing the preference relations. If there are multiple best alternatives, after eliciting additional strict preference relations from the decision makers, we
can eventually find a unique best one by solving the mathematical programming problem
with additional preference constraints.
Keywords: Multi-Criteria Decision Making.
258
TH4D (contributed)
Multicriteria 0-1 Knapsack Problems with K-min
Objectives
Rong, Aiying (*)
Technical University of Lisbon
Figueira, Jose
Klamroth, Kathrin
This paper studies the multicriteria 0-1 knapsack problem (KP) with k-min objectives
(MkMIN-KP) in which the first objective is of classical sum type and the remaining objectives are k-min objective functions. The k-min objectives are ordinal objectives, aiming
at the maximization of the kth smallest objective coefficient in any feasible knapsack solution with at least k items in the knapsack. The k-min objectives can be treated as a
generalized bottleneck objective. From a practical point of view, the MkMIN-KP is an
extension of KP by explicitly considering the ranking measures as objective criteria. The
ranking measures can be used to model the preferences of the decision makers or the objectives that are difficult to handle by traditional means. Next, we develop efficient hybrid
algorithms to obtain the complete non-dominated set of MkMIN-KP based on a variant of
the epsilon-constraint method by solving a series of single objective 0-1 multidimensional
KPs with a special structure.
Keywords: Hybrid Approaches, K-Min Objectives, Multi-Dimensional Knapsack Problem, Multi-Objective Optimization.
259
MO3C (contributed)
Multicriteria Cognitive Map: an Integrated Tool for
Building a Multicriteria Evaluation Model
Cipriano Rodrigues, Teresa (*)
Oliveira, Mónica Duarte
University of Lisbon
Multicriteria value measurement tools have been extensively used to build evaluation models, which often take a given set of alternatives and evaluation criteria as the starting-point
in model building. Nevertheless, it has been recognized that a very demanding and challenging task when building an additive evaluation model is to help the decision-maker to
structure the model, in particular constructing a set of criteria and associated descriptors
to be used in the evaluation of a set of decision alternatives. Few tools address, in a
coherent and integrated way, problem structuring and multicriteria evaluation of decision alternatives. This study proposes a tool – a Multicriteria Cognitive Map (MCM) –
which, by ameliorating the capability of making inferences in a cognitive map, supports
the construction of a multicriteria evaluation model. Developing a MCM integrates two
phases: a first phase to structure the problem by capturing the issues and their systemic
relationships in a means-ends network; and a second phase to determine the impact that
alternatives (means) have in the values (ends) of the decision-makers. This latter phase
uses the MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique) approach to measure the strength of the influence of each means-end link. A case
study based on the construction of a health index is used to illustrate how the MCM tool
can assist building a multicriteria evaluation model.
Keywords: Cognitive Map, MACBETH, Multi-Criteria Evaluation Model, Problem
Structuring.
260
TU5G (invited)
Multicriteria Evacuation Plan for Natural Disasters
Ndiaye, Ismaila Abderhamane (*)
Tours University
Neron, Emmanuel
Tours University
Since the 90s, the problems of evacuation of persons have been extensively studied in the
literature. The proposed models are classified intotwo main categories: macroscopic and
microscopic models. The DSS EVAC LOGISTIQUE project is interested in the evacuation of people in the context of flooding, burning or seismic event for which insecurity,
capacity and time of crossing roads vary over time. Considering as solved the shelter
locations problem, we develop discrete macroscopic models and methods incorporating a
notion of risk inherent of the context studied for the evacuation of persons. The problem will be to determine a minimum overall evacuation time while minimizing the risk
incurred by each evacuee. In this context, we propose two methods based on Shortest
augmenting Paths without using a time-expended network. A efficient exact pseudopolynomial algorithm to solve the Earliest Arrival Flow and the Quickest Flows problems
with time dependent data in graphs with one strongly connected component. An epsilonconstrains method for solving Quickest/Safest flows problem with time-dependent data.
This research has been supported by the French National Agency of Research ANR-11SECU-002-01- project DSS EVAC LOGISTIQUE.
Keywords: Dynamic Network, Earliest Arrival Flow, Evacuation, Macroscopic Model,
Multi-Objective Optimization, Quickest Flow, Safest Flow, Time Dependent Data, Transshipment.
261
FR3B (contributed)
Multicriteria Model for Locating Distribution Centers
Manotas, Luis A. (*)
Business Vice-President
Manotas, Diego F.
Universidad del Valle
Ocampo, Germán
Vidal, Carlos
The problem of location of distribution centers is one of the most important strategic decisions in the field of supply chain management. Usually, this decision has been analyzed
with qualitative tools like opinion of experts. In this paper, we propose an integrated
framework between real options analysis and multi-criteria decision methods. The idea is
to include the strategic value based on expansion options implied in this kind of decisions
like a one of the decision criteria to consider. The paper is supported on three subjects:
Models for locating of distribution centers, financial models applied to facilities location
problems and multi-criteria decision techniques. The methodology developed in this paper includes six types of criteria: financial risk (probability of success) , strategic value
(Expansion option), proximity to customers, proximity to production facilities, infrastructure and exposition to natural disasters. The model considered three different location
alternatives. To calculate the probability of success and the volatility parameter of each
possible location , we used Monte Carlo simulation. After that, we applied the binomial
model to value the possible expansion options implied in each location alternative. The
multi-criteria method utilized in this work was an AHP-TOPSIS hybrid model. AHP was
used to obtain the weights of each criterion and TOPSIS was applied to build a ranking
of all location alternatives. The main results show, if the options of expansion are not
considered, the best alternative from an economic point of view would be the alternative
without expansion option, by his better probability of success, but considering the real
options associated with expansion the results changed and the best choices were the alternatives with expansion possibilities.
Keywords: Distribution Centers Location Problem, Real Options Analysis, TOPSIS.
262
FR3B (contributed)
Multicriteria Model for Selecting Renewable Energy
Projects
Manotas , Diego F. (*)
Lozano, Carlos A.
Vidal, Carlos
Energy systems play a very relevant role in the social and economic development of a country. In this context, it is necessary to analyze the generation expansion planning (GEP)
problem, because decision makers in the energy sector require robust tools to balance
the energy demand and the power generation supply. Traditionally, the GEP problem
has been analyzed with economic techniques such as levelized cost. However, this is not
enough because this kind of problems is influenced by other criteria. One of the most
important tasks related with the GEP problem is the selection process among different
alternatives for the expansion of renewable energy sources. The problem under study is
very complex due to the consideration of different kinds of criteria such as environmental,
social, technical and economic factors. The multiplicity of criteria and the involvement
of different actors in the decision procedure make multi-criteria analysis a valuable tool
in the policy formulation for fossil fuel energy replacement by renewable energy sources.
In this paper, we introduce a multi-criteria hybrid model using the traditional Analytical
Hierarchy Process (AHP), the Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS), and considering financial risk indicators to prioritize a set of different
renewable energy projects in the Colombian electricity market. The methodology used in
this work begins with the definition of criteria. At this stage, we review the literature related with the application of multi-criteria analysis to define project portfolios in different
types of engineering projects. Later, in order to consider the uncertainty in the decision
making process, we include some financial risk indicators such as the Value at Risk (VaR)
and the Conditional Value at Risk (CVaR). Finally, we present some results about the use
of an integrated framework between financial risk indicators and multicriteria methods.
Keywords: Analytic Hierarchy Process, Electricity Production Technologies, Risk Analysis, TOPSIS.
263
WE2E (contributed)
Multicriteria Modeling and Optimization of a Market
Place of Leads
Manel, Maamar (*)
Mousseau, Vincent
Ouerdane, Wassila
Ecole Central Paris
Place des Leads (PdL) is a market place of ’lead’.This one faces a complex problem
namely ’the assignment of the leads to the customers’.On the one hand,PdL receives a
significant amount of leads in real time.On the other hand,the customers of PdL make orders and can filter the leads that they wish to receive according to multiple criteria.Unlike
the classical assignment problems in the literature,the challenge with this problem is to
take into account at the same time the optimization of multiple objectives and the real
time aspect as well.This work has the aim to tackle this problem.To do that,we propose
to formulate this problem as a multi-objective mathematical program.After that,we will
consider a stochastic model of the offer and request in the optimization step in order to
take into account the real time aspect and therefore the fact that the flows of offer and
request evolve continuously.More specifically,a first step is to consider the problem from a
single objective and to see afterwards to what extent it is possible to extend it to multiple
objectives.Thus,the assignment problem can be described by n orders and m leads,such
that the assignment of a lead to a given order is represented by a binary variable xij .
Therefore, the set X = {xij /i ∈ {1, . . . , n}, j ∈ {1, . . . , m}} is all possible combinations
of leads and orders. However,in our specific problem, we can note that a given order does
not accept any lead.In fact,a lead can be assigned to a given order only if it satisfies some
criteria called filters. Thus, some pairs xij are not possible.Moreover,some orders require
exclusive leads.Thus,we propose an algorithm of pretreatment which constructs two subsets:Xe which represents all xij such as i is an exclusive order and Xe’,the set of xij such
as i is a nonexclusive order. In addition,this algorithm checks whether a lead corresponds
to a command according to the filters.This will allow us to reduce the research space.
Finally,the problem is to find the optimal assignment.
Keywords: Assignment, Multi-Objective Optimization.
264
MO4F (invited)
Multicriteria Models Applied to Sustainable
Enviroment and Efficient Use of Energy
Alegr´ıa Mej´ıa, Lol-chen (*)
University of Mexico
Escobar-Toledo, Carlos Enrique
In a recent research about economic and environmental evaluation scenarios to 2030,
based on the potential for GHG in the Mexican energy system, we have included alternate
sources of energy for power generation as a case study. We propose in this presentation,
a planning approach considering different new technological alternatives from 2025 until 2040, using multiple criteria analysis and mathematical programming to satisfy the
demand every five years. Results are analyzed considering also a sensitivity analysis in
which is performed a Markov chain analysis.
Keywords: Electricity Production Technologies, Markov chains, Mathematical programming, PROMETHEE.
265
MO3B (contributed)
Multiobjective Evolutionary Approach to Preference
Elicitation in the Analytic Hierarchy Process
Mikhailov, Ludmil (*)
The paper is concerned with the problem of deriving priority vectors from pairwise comparison matrices, in the framework of the Analytic Hierarchy Process (AHP). With the
exception of the traditional Eigenvector method, most methods for deriving priorities in
the AHP are based on some optimization approach. All optimization methods for prioritization introduce a single objective function, which measures the degree of approximation
or the distance between the initial judgments and the solution ratios. Then the problem of
priority elicitation is formulated as a single-objective optimization task of minimizing the
objective function, subject to normalization and some additional constraints, and obtain
a single priority vector from a given comparison matrix. A new approach to prioritization in the AHP, which represents the prioritization task as a multiobjective optimization
problem was proposed recently by the author. A two-objective prioritisation (TOP) problem is formulated as an optimisation task for minimisation of the Euclidian norm and the
number of rank violations, which measure the most important properties of the solutions.
The paper investigates some evolutionary and numerical approaches for solving the TOP
problem. In order to eliminate the drawbacks of the numerical methods, we propose two
evolutionary solution approaches. In the first evolutionary approach, the TOP problem
is transformed into a single-objective one, which is then solved by a standard evolutionary algorithm (EA). We also investigate the application of a multiobjective evolutionary
algorithm (MEA) for solving the TOP problem. The numerical results show that the
MEA outperforms the gradient search method and the single-objective EA with respect
to accuracy and computational efficiency.
Keywords: Analytic Hierarchy Process, Evolutionary Multi-Objective Optimization,
Multi-Objective Optimization, Prioritisation Methods.
266
TU2F (contributed)
Multiobjective Land-Use Planning Based on
Proximity Measures
Stewart, Theodor (*)
University of Cape Town
We have previously discussed the problem of allocating different land uses across a region,
when an important concern is spatial contiguity of activities. These problems lie between
the two extreme cases which are simpler to solve than the general problem, namely optimization of land use for each parcel independently of others (no contiguity constraints),
and partitioning of the region into single contiguous areas for all land uses. In the intermediate case, the number of contiguous areas for each land use is not predetermined,
but the degrees of contiguity of each land use become decision criteria (in addition to the
usual criteria related to economic, environmental and social issues. Our original work was
based on a grid network for which efficient algorithms could be developed. The number
of grid cells needed to model the desired resolution at all points in the region became
prohibitively large for rapid solution in interactive systems. The alternative was to use
a vector-based representation of the region in terms of polygons of varying sizes. This
interfaced well with GIS systems in which the data were stored. This approach was in
principle much more efficient, but required users to extract quite complicated information
from the GIS, which can also be time consuming and for which requisite skills are not
always available. In this paper we discuss an alternative use of vector-based data, but
in which the algorithm looks only at distances between polygons. Such data is easily
extracted from the GIS. We present numerical results to show that this simpler approach,
linked to a specially designed genetic algorithm, produces results virtually indistinguishable from thos based on the original vector-based algorithm.
Keywords: Genetic Algorithms, Land Use Planning, Reference Point.
267
TU5E (contributed)
Multiobjective Optimization of High-Performance
Reinforced Concrete I-Beams by Simulated Annealing
Yepes, V´ıctor (*)
Alcalá, Julián
Garc´ıa-Segura, Tatiana
The present study aims to describe a methodology to design reinforced concrete I-Beams
based on multiobjective simulated annealing (MOSA) algorithm applied to three objective
functions, namely, the economic cost, the durability, and the overall safety of the structure. This methodology was applied to a 15 m beam span for several high-performance
concrete mix compositions. The evaluation of solutions follows the Spanish Code for
structural concrete. The solution of this simply supported concrete I-beam is defined
by a total of 20 variables. Pareto results of the MOSA algorithm suggest the feasibility
of more economic structures remaining in service for long periods and ensuring safety.
This methodology is widely applicable to a different structure designs and therefore, gives
to engineers a worthy guide of the benefits of economic, durable and safety perspectives.
Further, the methodology proposed will help structural engineers to enhance their designs
of reinforced concrete structures.
Keywords: Durability, Metaheuristics, Multi-Objective Optimization, Simulated Annealing, Structural Modeling.
268
TU4D (invited)
Multiobjective Optimization with Combined Global
and Local Metamodeling
Azarm, Shapour (*)
University of Maryland
Hu, Weiwei
Saleh, Khaled
Approximation Assisted Optimization (AAO) is widely used in engineering design problems to replace computationally intensive simulations with metamodeling. Traditional
AAO approaches employ global metamodeling for exploring an entire design space. Recent research works in AAO report on using local metamodeling to focus on promising
regions of the design space. However, very limited works have been reported that combine
local and global metamodeling within AAO. In this presentation, a new approximation
assisted multiobjective optimization approach is developed. In the approach, both global
and local metamodels for objective and constraint functions are used. The approach starts
with global metamodels for objective and constraint functions and using them it selects
the most promising points from a large number of randomly generated points. These selected points are then “observed”, which means their actual objective/constraint function
values are computed. Based on these values, the “best” points are grouped in multiple
clustered regions in the design space and then local metamodels of objective/constraint
functions are constructed in each region. All observed points are also used to iteratively
update the metamodels. In this way, the predictive capabilities of the metamodels are
progressively improved as the optimizer approaches the Pareto optimum frontier. An
advantage of the proposed approach is that the most promising points are observed and
that there is no need to verify the final solutions separately. Several numerical examples
are used to compare the proposed approach with previous approaches in the literature.
Additionally, the proposed approach is applied to a CFD-based engineering design example. It is found that the proposed approach is able to estimate Pareto optimum points
reasonably well while significantly reducing the number of function evaluations.
Keywords: Computationally Expensive Problems, Multi-Objective Optimization.
269
TU4D (contributed)
Multiobjective Variational Problems Involving
Generalized Higher Order Functions
Shukla, Kalpana (*)
Banaras Hindu University
We consider the following multiobjective variational problem as the following form:
Z
min
b
Z
1
f (t, x(t), x(t),
˙
x¨(t))dt, . . . , min
a
b
f k (t, x(t), x(t),
˙
x¨(t))dt
a
subject to x(a) = 0 = x(b),
x(a)
˙
= 0 = x(b),
˙
hj (t, x(t), x(t),
˙
x¨(t)) ≤ 0, j ∈ M ≡ {1, 2, . . . , m},
x ∈ P S(T, Rn ),
where functions f i , i ∈ K = {1, 2, . . . , k} and hj , j ∈ M = {1, 2, . . . , M } are continuously
differentiable functions defined on I × Rn × Rn × Rn .
In this paper we have established some optimality conditions for the multiobjective variational programming problems with generalized convexity of higher orders. A higher order
dual is associated and weak and strong duality results are established under generalized
convexity assumptions.
References:
[1] Aghezzaf, B., Khazafi, K. (2004): Sufficient optimality conditions and duality in multiobjective variational programming problems with generalized invexity; J. Control Cyber.,
Vol. 33, pp. 1-14.
[2] Bhatia, D. and Kumar, P. (1996): Duality for variational problems with vex functions;
Optimization, 36, 347-360.
Keywords: Daily Used Materials, Duality, Multi-Objective Optimization, Variational
Problems.
270
TH4F (contributed)
Multiple Criteria Decision Making in Anti-Collision
Scheduling for Level Crossing Tasks
Levchenkov, Anatoly (*)
Riga Technical University
The paper proposes the solutions for applying the MCDM, embedded devices, schedule
theory and immune algorithms to minimize simultaneous existence of a railway transport
and auto vehicle at a crossing, i.e. avoiding a possibility of collision. Nowadays transport
flows are increasing. The traffic congestion and accidents become an everyday reality.
It is hard to evaluate losses from the accidents and all these consequences for the state
in general and a separate citizen, but the negative effect is definitely proved. More serious consequences are colliding railway transport with auto vehicle. The main goal of
the research is to investigate and develop the algorithms of MCDM for road and railway
transport scheduling and control. The research includes the analysis of existing railway
and city transport system, the development of the mathematical models and multiple criteria schedule affinity assessment function of scheduling immune algorithms, antigen and
antibodies models and for anti-collision task and investigation. The computer model of
sequence of level-crossings with railway and road transport flows with stochastic parameters, such as traffic, technical condition, weather conditions, is created. Also prototypes
of embedded intelligent devices are manufactured to test is in real conditions. Set of more
than thousands experiments have been performed. The developed models and algorithms
may improve the safety level of transport system control. The schedules developed by the
immune algorithm provide better results than the original schedule in 100% cases by all
target function criteria. Essential reduction of collision probability is possible from the
original value 0.94 to result value 0.01. The research leading to these results has received
funding from the ARTEMIS Joint Undertaking and from Latvian Academy of Science
and Ministry of Education and Science under grant agreement n◦ 295373.
Keywords: Embedded Systems, Multi-Criteria Decision Making, Scheduling.
271
TH3B (invited)
Multiple Criteria Hierarchy Process for ELECTRE
TRI Methods
Greco, Salvatore
Slowinski, Roman
We are considering the hierarchy of criteria in ELECTRE TRI methods designed for multicriteria sorting problems. We discuss several possible options for this generalization,
in particular: - with respect to construction of the outranking relation in the context of
hierarchical criteria, we shall consider: o outranking relation built in the way of ELECTRE IS method, o outranking relation built in the way of ELECTRE III method; - with
respect to reference profiles, we shall consider: o boundary reference profiles separating
consecutive classes (ELECTRE Tri-B), o central reference profiles characterizing particular classes (ELECTRE Tri-C); - with respect to the number of reference profiles, we shall
consider: o single and multiple boundary reference profiles (ELECTRE Tri-B), o single
and multiple central reference profiles (ELECTRE Tri-C, ELECTRE Tri-nC); - with respect to the sorting procedure for ELECTRE Tri-B, we shall consider: o the optimistic
procedure, o the pessimistic procedure; - with respect to interaction among criteria, we
shall consider: o the classical concordance index without interaction among criteria, o
the concordance index with possibility of mutual strengthening effect, mutual weakening
effect and antagonistic effect, o effects of reinforced preference and counter-veto in credibility of outranking. We shall also discuss inference of parameters using Robust Ordinal
Regression (ROR) and using Stochastic Multiobjective Acceptability Analysis (SMAA).
Finally, we shall present a didactic example that illustrates the methodology with the
above variety of options.
Keywords: Decision Making, Multi-Criteria Hierarchy Process, Outranking Methods.
272
MO3D (contributed)
Multiple Criteria Methods in Decision Support
Systems for Forest Management
Segura, Marina (*)
Maroto, Concepción
Ray, Duncan
Centre for Human and Ecological Sciences
In decision making the most appropriate methods to solve a particular problem usually
depend on specific relevant characteristics. This work provides an in-depth analysis and
assessment of methods used in Decision Support Systems (DSS) for sustainable forest
management. The problem nature has been considered through the following dimensions:
temporal scale, spatial context, spatial scale, number of objectives and decision makers
or stakeholders and goods and services involved. Some of these dimensions were significantly related and demonstrated using contingency tables. The objective dimension had
a significant influence on the distribution of forest problems by temporal scale, single or
multiple makers/stakeholders and the types of goods and services. For example, multiple
objectives were more frequently set in strategic problems than in tactical and operational
issues. We also found significant relationships between the methods and problem dimensions. The results showed that three out of four forest management problems have
multiple objectives, but the percentage solved by Multiple Criteria Decision Making is
only 40%. Analytic Hierarchy Process (AHP), Goal Programming, Multi-Attribute Value
(MAV) and Multi-Attribute Utility Theory (MAUT) are the most frequent approaches
in DSS for forest management, especially the former (AHP). The analysis demonstrated
that the majority of DSSs are focused on market products, alone or together with services. Globally, at least of one third of these include multiple criteria approaches including
also group decision making. In this situation multiple criteria and group decision making
should be developed further in DSSs to provide a higher contribution to decision making
in forestry sector.
Keywords: Decision Support Systems, Forest Management, Group Decisions Making,
Multi-Criteria Decision Making, Multi-Objective Optimization.
273
WE2F (invited)
Multivalued Heuristics for One-To-One Biobjective
Shortest Path Problems
Machuca, Enrique (*)
Universidad de Málaga
Mandow, Lorenzo
We consider the problem of calculating the set of all Pareto-optimal solutions to a one-toone biobjective shortest path problem. Recent analyses have revealed that the efficiency
of multiobjective label-setting algorithms improves with the use of consistent informed
heuristic estimates. In particular, the vector-valued precalculated heuristic proposed by
Tung and Chew (1992) has been extensively tested over the NAMOA* algorithm. The
Tung-Chew heuristic is by definition the most informed admissible heuristic that can
be obtained using a single vector estimate for each node. However, in theory multivalued heuristics, that provide several vector estimates for each node, can still result in
even more informed heuristics. We extend the method proposed by Tung and Chew to
calculate multi-valued heuristics for biobjective problems, analyze their properties, the
requirements of managing multiple heuristic estimates, and evaluate the improvements in
space and time requirements that can be obtained from them.
Keywords: A* Search, Artificial Intelligence, Bi-Objective Shortest Path Problems,
Heuristic Search.
274
WE2D (invited)
Natural Gas Pipelines: A Decision Support System to
Evaluate Multidimensional Risk
Alencar, Marcelo (*)
Universidade Federal de Pernambuco
Almeida, Adiel
Clemente, Thárcylla
Krym, Eduardo
This paper presents a Decision Support System (DSS) that is able to support evaluating
risks in natural gas pipelines by taking a multicriteria decision analysis into account. The
use of a multi-criteria approach to risk assessment and analysis allows different aspects
directly linked with the problem to be considered. In this type of approach, there is a
need to collect technical, social, and environmental data relating to the pipeline sections
that are to be evaluated. In addition, factors related to the decision-making process are
considered as well as human value judgments.
Keywords: Decision Support Systems, Multi-Criteria Decision Analysis, Natural Gas
Pipeline, Risk.
275
TH4C (contributed)
New Alternative Aggregation Formulas for the Human
Development Index: a Multicriteria Approach
Rodr´ıguez D´ıaz, Beatriz (*)
Angulo-Guerrero, Mar´ıa José
Luque, Mariano
Pérez-Moreno, Salvador
Since its publication in 1990 by the United Nations Development Programme (UNDP),
the Human Development Index (HDI) has generated an extensive literature that has assessed its properties, provided numerous critiques, and proposed a number of potential
improvements. In 2010, coinciding with the twentieth anniversary of the launching of
the first Human Development Report (HDR), the UNDP decided to revise the HDI and
introduced several major changes. Many of the problems pointed out by critics were tackled with the changes introduced into the way to calculate the new HDI, although some
authors considers that drawbacks still persist, particularly, in relation to the problematic
tradeoffs between the HDI’s components. In this work we propose alternative aggregation
formulas for the HDI and assess the problem of substitutability between the HDI’s three
components. To this end, we carry out an approach based on the double reference point
methodology (aspiration-reservation). For each indicator, the value of each country is
normalized by means of two reference values (aspiration and reservation values) by using
an achievement scalarizing function which is piecewise linear. Aggregating the new values
(values of the achievement scalarizing functions), we calculate a weak indicator that allows compensation among the various components, a strong indicator that measures the
state of the worst component, allowing no compensation, and a mixed indicator, which is
a linear combination of these previous ones. Subsequently, the three rankings of countries
resulting from the respective indicators calculated are compared to the HDI ranking, evidencing the existence of significant tradeoffs across IDH dimensions that may distort the
real measurement of progress in human development among countries.
Keywords: Aspiration and Reservation Values, Human Development Index, MultiCriteria Decision Analysis, Reference Point, Substitutability between components, Synthetic indicators.
276
TU3E (invited)
New Developments of Interactive Multiobjective
Optimization using Dominance-Based Rough Set
Approach
Matarazzo, Benedetto (*)
Greco, Salvatore
Slowinski, Roman
Application of Dominance-based Rough Set Approach (DRSA) in Interactive Multiobjective Optimization (IMO) gave rise to a new method employing a logical preference model.
It is composed of two alternating stages: computation stage, and dialogue stage. In the
computation stage, a sample of feasible solutions is generated and presented to the Decision Maker (DM). In the dialogue stage, the DM indicates relatively good solutions in the
generated sample. This binary classification of sample solutions into ‘good’ and ‘others’
is an input preference information for DRSA. DRSA is using this information to induce a
preference model expressed in terms of “if. . . , then . . . ” decision rules. The DM is then
asked to select one decision rule considered the most interesting, which defines new constraints to be added to the constraints of the previous problem, cutting-off non-interesting
solutions from the former optimal set. A new sample of solutions is generated in the next
iteration from the reduced optimal set. The interaction continues until the DM finds
a satisfactory solution. DRSA permits also to describe the Pareto frontier in terms of
“if. . . , then . . . ” association rules. We present further developments of this methodology
related to 1) possibility of selecting more than one constraining rule in each iteration; 2)
possibility of considering also rules describing relatively bad solutions; 3) possibility of using DRSA not only to describe the Pareto frontier, but also the space of decision variables.
Keywords: Dominance-Based Rough Set Approach , Interactive Multi-Objective Optimization, Preference Information.
277
MO5E (award)
New Directions in Robustness Analysis and Preference
Modeling in Multiple Criteria Decision Aiding
Kadzinski, Milosz (*)
Pozna´
n University of Technology
We propose a set of novel decision aiding methods based on two prevailing preference
models: additive value function and outranking relation. The introduced approaches take
into account preference information of a new type and conduct robustness analysis of the
suggested recommendation in an innovative way. The proposed robust ordinal regression procedures account for indirect, imprecise, and incomplete preference information
provided by the decision makers (DMs). This includes the traditionally used pairwise
comparisons or assignment examples, which are, however, decomposed in an original way
to parameters of the generalized outranking- or value-based models. Additionally, we
consider other types of preference information which so far has not received due attention
in Multiple Criteria Decision Making. These are rank-related requirements in the form of
desired positions and scores, possibly imprecise desired class cardinalities, or real intervals for indifference and preference thresholds. When it comes to robustness analysis, we
present new methods for exploiting the consequences of applying all preference models
compatible with the provided preferences. In particular, we implement reasoning in terms
of the necessary and possible consequences for outranking methods and group decision
problems. We also propose the framework of extreme ranking analysis and procedures
for selection of a representative preference model instance. In this way, the introduced
approaches identify the recommendation observed in case of all, some, the most favorable,
the least advantageous, or the most robust compatible model for the considered preferences. When confronted with the value system of the DM, these results can be used for
looking more thoroughly into the subject, by exploring, reasoning, or testing scenarios.
Thus, the presented methods promote alternate phases of preference elicitation and robustness analysis as a versatile tool for approaching real-world decision problems.
Keywords: Additive Value Function, Decision Aiding, Outranking Relation, Preference
Disaggregation, Preference Modeling, Robust Ordinal Regression, Robustness Analysis.
278
MO3E (contributed)
NIMBUS in Interactive Multiobjective Optimization
under Uncertainty
Miettinen, Kaisa (*)
Mustajoki, Jyri
Finnish Environment Institute
Stewart, Theodor
University of Cape Town
We propose an interactive method for decision making under uncertainty, where uncertainty is related to the lack of understanding about consequences of actions. Such
situations are typical especially in design problems, where a decision maker has to make
a decision about a design at a certain moment of time even though the actual consequences of this decision can be possibly seen only many years later. To overcome the
difficulty of predicting future events when no probabilities of events are available, our
method utilizes groupings of objectives or scenarios to capture different types of future
events. Each scenario is modelled as a multiobjective optimization problem to represent
different and conflicting objectives associated with the scenarios. We utilize the interactive classification-based multiobjective optimization method NIMBUS for assessing the
relative optimality of the current solution in different scenarios. This information can be
utilized when considering the next step of the overall solution process. Decision making
is performed by giving special attention to individual scenarios. We demonstrate our
method with an example in portfolio optimization.
Keywords: Classification, Decision Making, Interactive Methods, Multi-Objective Optimization, NIMBUS, Pareto Optimal Solutions, Scenarios, Uncertainty Handling.
279
TU2E (invited)
Non Additive Robust Ordinal Regression for Urban
and Territorial Planning: an Application for Siting an
Urban Waste Landfill
Bottero, Marta (*) Ferretti, Valentina
Angilella, Silvia
Lami, Isabella
Corrente, Salvatore Greco, Salvatore
Multiple Criteria Decision Aiding (MCDA) gives not only a toolbox, but, overall, a well
developed methodology to support decision-making processes. MCDA is particularly useful in the context of sustainability assessment and urban and territorial planning, where
a complex and inter-connected range of environmental, social and economic issues must
be taken into consideration. Moreover, the analysis of the possible interactions among
the considered criteria is of particular importance for assessing the sustainability of a
certain transformation. The paper proposes an application of the Non Additive Robust
Ordinal Regression approach for the selection of a new landfill location among several alternatives. NAROR aggregates information on the considered criteria using the Choquet
integral in order to represent the interactions among criteria by means of non-additive
weights, technically called fuzzy measures. Using NAROR, the DM can supply preference information in terms of pairwise preference comparisons, intensity of preferences,
pairwise comparisons on the importance of criteria and sign and intensity of interaction
among pairs of criteria. The basic idea of NAROR is to consider the whole set of the
fuzzy measures that are compatible with the preference information given by the DM. In
fact, the recommendation supplied by NAROR is expressed in terms of necessary preferences, in case an alternative is preferred to another for all compatible non-additive weight
vectors, and of possible preferences, in case an alternative is preferred to another for at
least one compatible non-additive weight vector. In the considered case study, several
sites for the location of the landfill are analyzed and compared through the use of the
NAROR on the basis of different criteria, such as presence of population, hydrogeological
risk, interferences on transport infrastructures and economic cost.
Keywords: Fuzzy Measures , Interaction Among Criteria, Multi-Criteria Decision Aid,
Sustainability Assessment.
280
TU3E (invited)
Non-Linear Multi-Objective Optimization via Parallel
Algorithms: Solving a Planar Facility Location and
Design Problem
López-Redondo, Juana (*)
Gila-Arrondo, Aránzazu
University of Almer´ıa
A new multi-objective evolutionary algorithm, called FEMOEA, which can be applied
to many nonlinear multi-objective optimization problems has been recently proposed. It
combines ideas from different multi- and single-objective optimization evolutionary algorithms, although it also incorporates new devices, namely, a new method to improve the
efficiency of points using gradient information and a new stopping rule, which help to
improve the quality of the obtained approximation of the Pareto-front and to reduce the
computational requirements. Even so, when the set approximating the Pareto-front must
have many points (because a high precision is required), the computational time needed
by FEMOEA may be not negligible at all. Furthermore, the computational resources
needed may be so high that a PC may run out of memory. In those cases, parallelizing
the algorithm and run it in a supercomputer may be the best way forward. In this work,
a parallelization of FEMOEA, called FEMOEA-Paral, is presented. To show its applicability, a bi-objective competitive facility location and design problem is solved. As the
results will show, FEMOEA-Paral is able to maintain the effectiveness of the sequential
version and this by highly reducing the computational costs. In fact, super efficiency
values were obtained independently of the number of available processors. Furthermore,
the parallel version shows a good scalability.
Keywords: Bi-Objective Location Problem, Effectiveness, Efficiency, Non-Linear MultiObjective Optimization, Parallel Algorithm.
281
MO4B (invited)
Nonconvex Pareto Navigator for Interactive
Multiobjective Optimization
Hartikainen, Markus (*)
Klamroth, Kathrin
Miettinen, Kaisa
This talk describes a new interactive method called Nonconvex Pareto Navigator which
extends the convex Pareto Navigator method for nonconvex multiobjective optimization
problems. The Nonconvex Pareto Navigator method enables moving on an approximation
of the Pareto front in the objective space so that the decision maker can find preferable
compromise values for the objectives. Navigating on the approximation allows fast computation and, thus, a better user experience for the decision maker, when compared
to navigating on the actual Pareto front. The method is especially useful in solving
computationally expensive real-life multiobjective optimization problems where function
evaluations may be time-consuming. The proposed Nonconvex Pareto Navigator method
consists of two essential parts: constructing a Pareto front approximation and navigating
on it. In this talk, besides introducing the new method, we discuss quality criteria - both
mathematical and those related to the decision maker’s user experience – which the approximation and the navigation should fulfill. Finally, we demonstrate how our proposed
Nonconvex Pareto Navigator performs with respect to those criteria.
Keywords: Decision Making, Interactive Methods, Multi-Objective Optimization.
282
WE1C (contributed)
On a Linear Transformation of Variables in Data
Envelopment Analysis
Korhonen, Pekka (*)
Aalto University
Keshvari, Abolfazl
Aalto University
In this presentation, we consider a linear transformation of the input- and output-variables
in the Data Envelopment Analysis. When a linear transformation is non-singular, no
problems arise provided the efficient frontier is re-defined, respectively. Instead, a singular linear transformation is much more complex. This is the case, when we reduce the
number of inputs and/or outputs. We will consider two dimension reduction methods
commonly applied in the Data Envelopment Analysis (DEA) and point out that a user
has to be very careful in choosing the method. In reduction of the number of output/input-variables, there are several pitfalls, which may lead to wrong results. We will
discuss these pitfalls and illustrate problems with numerical examples.
Keywords: Data Envelopment Analysis, Linear Transformation, Variable Reduction.
283
TU3C (contributed)
On Application of Multi-Criteria Decision Making
with Ordinal Information in Elementary Education
Mazurek, Jiˇr´ı (*)
Silesian University in Opava
In the Czech Republic each elementary or secondary school decides which textbook will be
used for a given class and a given subject of education. As a supply of textbooks is wide,
a selection of the most suitable textbook by a teacher is a typical case of multi-criteria
decision making situation where an evaluation of different textbooks on selected criteria
is rather ordinal in nature than cardinal, as it is not possible to assign textbooks some
numerical value with regard to criteria such as content, comprehensibility, adequacy to
children’s age and knowledge, etc. (with the exception of textbook’s price), but textbooks
can be ranked from the best to the worst by such criteria, and the best textbook can be
found by a new and simple mathematical method developed for this purpose in this paper. The aim of the paper is to show how this multi-criteria decision making method
with ordinal information can be used for the selection of the most appropriate textbook
for elementary science education, because a right choice of a textbook plays an important
role in children’s education. And we shall not forget that decisions made today influence
the world tomorrow, and the World of Tomorrow is also a World of Our (well-educated)
Children.
Keywords: Elementry Education, Multi-Criteria Decision Making, Ordinal Information.
284
FR2E (invited)
On Approximate Kernels of Minimal Size for Bicriteria
Problems
Jamain, Florian (*)
LAMSADE
Bazgan, Cristina
LAMSADE
Vanderpooten, Daniel
LAMSADE
Most multicriteria combinatorial optimization problems admit families of instances for
which the number of non-dominated points is exponential in the size of the instance. For
computational, as well as cognitive reasons, it is often relevant to generate a reduced
subset of solutions that represents as well as possible the different choices, i.e. providing
a good approximation of the Pareto set. This idea is represented by the concept of an ePareto set, which is a set of solutions that approximately dominates every other solutions,
i.e. such that for every solution S it contains a solution S’ that is better within a factor
(1+e) than S in all the objectives. Note that there may exist several e-Pareto sets of very
different size. An interesting problem studied by Vassilvitski and Yannakakis (2005) and
Diakonikolas and Yannakakis (2009) is the efficient construction of an e-Pareto set of size
as small as possible. In spite of this, such small e-Pareto sets may contain pairs of solutions such that one of them e-dominates the other one. Therefore, it may be interesting
to impose as an additional constraint that an e-Pareto set should only contain solutions
which are pairwise incomparable relatively to the e-dominance relation. Such e-Pareto
sets are called e-kernels. In this work, we propose generic algorithms that compute in
polynomial time an e-kernel of size as small as possible. We show some general results
about e-kernels and present a polynomial time algorithm to construct small e-kernels under some conditions for the bicriteria case. We show that an e-kernel does not always
exist when the number of criteria is at least three.
Keywords: Approximation Algorithm, Minimal Size, Multi-Criteria Decision Model,
Pareto Set.
285
TH4D (contributed)
On Branch and Bound Approach to Multi-Objective
Global Optimization
Zilinskas, Antanas (*)
Vilnius University
Zilinskas, Julius
Vilnius University
The first part of the talk is on the worst case optimal algorithms for Lipshitzian objectives. Nonconvex multi-objective optimization problems are considered. A result of single
objective global optimization is generalized showing that the worst case optimal algorithm
for approximation of Pareto front is reducible to the covering the feasible region by balls of
minimum radius. The adaptive and passive algorithms are coincident. A version of onestep optimal algorithm is analyzed in detail and implemented using the branch-and-bound
approach. Numerical examples are provided to compare the developed algorithm with an
algorithm optimal in average, and with some metaheuristics. The second part of the talk
concerns practical applications of the algorithms based on branch-and-bound approach.
Two combinatorial problems are considered: machine grouping for manufacturing, and
drawing aesthetically appealing graphs. Cell formation problem aims at identification of
groups of machines to form manufacturing cells. The problem involves conflicting objectives, for example, the total intercell moves of parts are minimal when all machines are in
a single cell, but the within-cell load variation is minimal when each machine composes
a separate cell. In this paper we propose a bi-criterion branch and bound algorithm for
cell formation problem. Two bi-criterion models were used: minimization of the numbers
of exceptional elements and voids, and minimization of intercell moves and within-cell
load variation. We demonstrate the performance of the algorithm solving problems from
the literature and compare the results with those of metaheuristic algorithms from the
literature. The second applied problem is related to the drawing of special graphs related
to business process management. The problem is reformulated as a multi-objective optimization problem where the quantitatively measurable aesthetic criteria are involved. An
algorithm is justified and numerical examples are provided.
Keywords: Approximation, Branch and Bound, Combinatorial Optimization, MultiObjective Optimization.
286
TH2F (contributed)
On Continuation Methods for Non-Linear
Martin, Benjamin (*)
Goldsztejn, Alexandre
Granvilliers, Laurent
Jermann, Christophe
Recent advances in Non-Linear Multi-Objective Optimization (NLMOO) have focused on
the use of continuation methods that exploit the continuity of solutions satisfying the
first order optimality conditions. Given a single solution, continuation methods allow the
inexpensive computation of a full component of connected solutions. The growing interest
in combining continuation with NLMOO methods is noticeable in the recent literature.
For instance, in Sch¨
utze et al. (2008) and Harada et al. (2007), continuation is associated
with metaheuristics (respectively a Particle Swarm Optimizer and a Genetic Algorithm)
in order to build solutions well scattered upon the Pareto set. With a similar goal, Pereyra
et al. (2009-2011) propose a continuation coupled with global optimization techniques in
the bi-objective case. Within a set-oriented framework, Sch¨
utze et al. (2005) use continuation to fasten the computation of new portions of the Pareto set, and to recover
connected parts possibly missed by the algorithm. Lovison (2011-2012) proposes a deterministic approach, using Delaunay tessellations and continuation to cover sets of solutions
satisfying optimality conditions. This approach is global provided the tessellation cuts
every connected components of solutions. We propose a comparative overview of such
NLMOO methods using continuations with the objective of showing their potential and
weaknesses. One discussion of special interest is their ability to handle constrained NLMOO. We also introduce a novel rigorous continuation method based on recent advances
in Constraint Programming. Used for NLMOO, it can in principle reconstruct a certified
enclosure of the whole Pareto-optimal set provided one solution in each component of this
set is given, i.e., under assumptions similar to Lovison (2011). Finally, we discuss how
such initial guesses could be obtained.
Keywords: Constraint Programming, Hybrid Approaches, Multi-Objective Optimization, Non-Linear Multi-Objective Optimization, Path-Following Methods.
287
WE1D (invited)
On the Need for Multi-Criteria Modelling in Power
Systems Analysis and Planning
Bertsch, Valentin (*)
Fichtner, Wolf
The promotion of renewable energy sources (RES) and combined heat and power generation leads to an increasing decentralisation of energy systems and brings about new
challenges. Especially in Germany, the realisation of the ambitious targets concerning the
expansion of RES necessitates an extensive structural rearrangement of the power system. For instance, the number of players in the market recently increased in consequence
of the decentralisation and is expected to further increase. Since the different players
typically pursue different objectives and have different preference perceptions, multiple
and usually conflicting targets need to be considered. As a result, decision processes need
to be designed in a participatory way. Moreover, a purely economic optimisation is no
longer sufficient to support decision making in energy systems since the importance of
ecological, technical and socio-psychological criteria steadily increases. We therefore call
for combining multi-criteria decision analysis (MCDA) and energy systems modelling approaches. Because of its transparent nature, multi-attribute value theory (MAVT) seems
to be suitable to bring together quantitative (e.g. economic, ecological) and qualitative
(e.g. acceptance) information in such participatory decision processes. Energy systems
models can be used to generate quantitative input data for the multi-criteria analysis.
Therefore, a multi-period linear optimisation model is presented, which provides a regional capacity expansion planning approach. The model is applied in a scenario-based
analysis in order to analyse the impact of the uncertainties associated with the long-term
development of the power system. Moreover, sensitivity analysis techniques are presented,
which may facilitate the process of preference elicitation by exploring the robustness of
the analysis with respect to variations of the subjective parameters. Finally, possible
further developments of model and methodology are discussed.
Keywords: Decision Maker’s Preferences, Linear Programming, Multi-Attribute Value
Theory, Participatory Multi-Criteria Evaluation, Sensitivity Analysis, Uncertainty Handling.
288
MO2B (contributed)
On the Optimal Inputs Balance in Terms of the
Educational Multiobjective Model: the Case of
Primary and Secondary Education Students
Luque, Mariano (*)
López Agudo, Luis Alejandro
´
Marcenaro-Gutiérrez, Oscar
This paper sheds new light on the relationship between inputs and outputs in the framework of the educational production function. In particular it is intended to gain a better
understanding of which factors may be affected to achieve an optimal educational outputs
level. To this aim we analyze teacher and test-based assessments in particular subjects
using a multiobjective schema. For much of the analysis we use data from a very recent
(2010) Survey (focused on Education and Housing in Andalusia) linked, on one hand, with
the results from an educational assessment program conducted among 10 and 14 year students (containing information on a wide set of per-sonal, family and school environment
characteristics) and, on the other hand, with the adminis-trative records on teacher-based
scores. These data are used to build up a mixed integer multiobjective model, on the basis
of a previous statistical and econometric analysis of these data. Then, a method based
on a reference point approach is used to determine the profile of the most ’successfully
-balanced’ students in terms of educational outcomes. Using appropriate reference levels
and weights, reference point methodology allow to generate “very balanced” solutions in
terms of the objective function values (educational outcomes). Finally, a sensitive analysis
is used to determine policies than can be carried out in order to increase the performance
levels of primary and secondary education students. The paper thus offers a new and
crucial contribution to the debate on education policies, particularly relevant in a context
of economic crisis.
Keywords: Econometrics, Education, Multi-Objective Optimization, Reference Point.
289
MO2B (contributed)
Optimization of Investment in Shares by
Brauers, Willem K. M. (*)
Vilnius Gediminas Technical University
Is it possible to use MOO for the selection of shares on a Stock Exchange such as shares
of the DJI New York, FTSE100 London, Nikkei225 Tokyo, CAC40 Paris etc? The as best
selected shares would be the outcome of eight objectives. Weights in many MO-methods
have a double function: normalization of the different units and giving importance to
the objectives. By obtaining dimensionless measures through the reading of the matrix
in a vertical way normalization is not needed. Up till now all objectives were based on
equal importance. The introduction of the opinions of the analysts will give more importance to some objectives. For the FTSE100 the matrix is composed of 100 shares to 9
objectives or in total 900 elements. Two different methods under the name of ’MOORA’,
namely a Ratio System and a Reference Point Approach, will try to make optimal the
content of the matrix. By adding the Full Multiplicative Form, three methods based on
dimensionless measures will be used. They will compose ’MULTIMOORA’, whereas the
three methods will control each other. An Ordinal Dominance Theory ranks the outcomes
of the three methods. The Belgian Bell20 Index is used as an example as it presents a
smaller exercise without too many difficulties to find the necessary data. However the
composition of the Belgian Index changes regularly making comparisons over time much
more difficult. In addition, the financial importance of Belgium diminished considerably.
Some General remarks: 1) the application concerns the past. Pure extrapolation has no
sense for such a fluctuating market. Regularly revisions are needed 2) companies with an
effective management are assumed. A thorough study work on each company is necessary
3) there are the Unknown Unknowns or may we say the Economics of Uncertainty? This
investigation is just an exercise, but with the aim how to operate similar studies. Anyhow
it has to be stressed that never before such a study for another World Index on shares
occurred.
Keywords: Finance, Multi-Objective Optimization, Multimoora, Multiplicative Form,
Ratio Analysis, Reference Point.
290
MO3F (contributed)
Optimization of the Operation of the Auxiliary
Services of Power Plants using Preference-based
Evolutionary Optimization
Ruiz, Ana Belén (*)
Cabello, Jose Manuel
Luque, Mariano
Ruiz, Francisco
The auxiliary services of a power plant are required for the plant operation. While they
are not the main components, their energy consumption is often significant, and it can
be reduced by implementing a series of strategies, like installing capacitors in different
parts of the network, changing current motors by high efficiency ones and/or installing
variable speed drives. Carrying out these improvements has a direct impact on the electricity consumption of the auxiliary systems and, thus, on the electricity required for the
plant operation. Overall, the interest of these energy savings lies, on the one hand, on the
reduction of the electricity production costs and, on the other hand, on the decrease of
the CO2 emissions. However, the cost of implementing these changes can be very high,
and it has to be evaluated. Moreover, a further economic analysis should be considered in
order to maximize the profitability of the investment. Therefore, we have proposed a multiobjective problem that determines which strategies are most suitable to maximize the
energy saving, to minimize the economic investment and to maximize the Internal Rate
of Return of the investment. In order to solve this multiobjective problem together with
a decision maker (DM), we have designed a process which progressively interacts with
him/her in order to, firstly, describe the auxiliary services to be studied and, secondly, to
find the most preferred solution according to his/her preferences, which are indicated by
means of desirable objective function values (a reference point (RP)). In that resolution
process, the evolutionary approach NSGA-II is applied to analyze the trade-offs among
the objectives, and, subsequently, the preference-based evolutionary algorithm WASF-GA
is used to approximate the reachable area of the nondominated objective set from the RP.
Finally, we have solved the problem associated to the auxiliary services of a particular
case study together with a real DM.
Keywords: Auxiliary Services Of Power Plants, Multi-Objective Optimization, PreferenceBased Evolutionary Algorithms, Reference Point.
291
TH2F (contributed)
Optimization over the Weakly Efficient Set of a
Multiobjective Programming Problem Constrained by
Quadratic Functions
Yamada, Syuuji (*)
Niigata University
Tanaka, Tamaki
Niigata University
Tanino, Tetsuzo
Osaka University
In this talk, we consider a multiobjective programming problem (MOP), where all objective functions are linear and all constrained functions are quadratic. It is known that
many multiobjective nonlinear programming problems can be transformed into or approximated by (MOP). If (MOP) is constrained by a single quadratic function, the weakly
efficient set can be defined as the intersection of a closed convex cone and the boundary
of the feasible set. Based on this fact, we formulate a quadratic programming problem
(QP) for checking the weak efficiency of a given feasible solution in the case where (MOP)
is constrained by multiple quadratic functions. Moreover, it is shown that (QP) can be
solved by listing Karush-Kuhn-Tucker points. By incorporating such the techniques into
the standard nonlinear optimization algorithms, we propose a globally optimization algorithm for a problem (MP) to minimize another linear function over the weakly efficient
set. An example of (MP) is furnished by the portfolio optimization problem in capital
markets. A fund manager may look for a portfolio which minimizes the transaction cost
on the efficient set. In order to verify the effectiveness of the proposed algorithm, we show
the computational experiments.
Keywords: Global Optimization, Multi-Objective Optimization.
292
TU5C (contributed)
Optimizing Economic and Environmental
Performances of Solar Power and Electric Vehicles: A
MOMILP Application
De Schepper, Ellen (*) Lizin, Sebastien
Hasselt University
Gandibleux, Xavier
Van Passel, Steven
Martin, Benjamin Vincent, Thomas
The EU has set targets for reducing its greenhouse gas emissions progressively up to 2050.
Recognizing that the sectors of heat & electricity generation and transport are the world’s
largest contributors to climate change, the deployment of clean energy and transportation technologies is widely stimulated. Unfortunately, better environmental performances
often imply higher economic costs. This trade-off between two conflicting objectives calls
for a multi-objective optimization (MOO) assessment, aiming to find the “best” possible
solutions, i.e. the Pareto optimal solutions. Whereas the economic and ecological optimization of energy systems is extensively studied in literature, little research has been
done on transportation systems. Furthermore, we argue that it is valuable to simultaneously optimize energy and transportation systems for two reasons. First, most entities
(e.g. firms, areas, individuals) have needs regarding both energy and transportation. Second, when considered simultaneously, synergies between the energy and transportation
systems can be exploited. This paper aims at filling this gap by performing a MOO on
a Belgian case study, i.e. a SME having a certain need for electricity and traveling. The
considered energy technologies are solar photovoltaics and grid electricity; for transport
internal combustion engine vehicles, grid powered battery electric vehicles (BEVs), and
solar powered BEVs are available. Aiming to obtain realistic results, the possible existence
of scale economies is taken into account. The latter implies a mixed integer programming
problem. Accordingly, this paper applies the exact algorithm described in: T. Vincent, F.
Seipp, S. Ruzika, A. Przybylski, X. Gandibleux; Multiple objective branch and bound for
mixed 0-1 linear programming: Corrections and improvements for the biobjective case.
Computers & Operations Research, 40(1)498-509, 2013. Additionally, the impact of policy measures on the Pareto front is visualized.
Keywords: Case Example, Mixed Integer Programming.
293
TU4F (contributed)
Optimizing Multi-objective Mixed Integer Programs
using Evolutionary Algorithms
Stidsen, Thomas (*)
Solving Multi-Objective Mixed Integer Program (MOMIP) models is notoriously difficult.
The standard techniques are the Two-Phase method or the Epsilon method, which both
rely on solution of Single Objective MIP’s. But if the solution of the single-objective MIP
is too hard, both methods fail. On of the more successfull approaches to solve MultiObjective optimization problems has been Evolutionary Algorithms like e.g. NSGA. Traditionally meta-heuristic algorithms has had problems solving Mixed Integer Programs,
primarily because it is hard to ensure feasibility of the solutions, given non-trivial constraints. Just generating feasible start solutions is (NP) hard. In this presentation I will
present a novel approach where an Evolutionary Algorithm is used, in combination whith
a standard MIP solver, to solve heuristically, the MOMIP, finding a heuristic Pareto front.
By using the MIP solver, the traditional problems with feasibility are avoided. A standard MIP solver is used for crossover and mutation operators and any of the well-known
evolutionar algorithm schemes, e.g. NSGA can be used for selection.
Keywords: Evolutionary Computations, Evolutionary Multi-Objective Optimization,
Integer Programming, Mixed Integer Programming, Multi-Objective Optimization.
294
WE2E (contributed)
Optimizing Telecommunications in Vehicular
Networks with a Parallel Multiobjective PSO
Toutouh, Jamal (*)
Alba, Enrique
This work presents pSMPSO, a master/slave version of the Speed-constrained Multiobjective Particle Swarm Optimization (SMPSO) algorithm that runs mutation and evaluation in parallel to accelerate the search. Our pSMPSO proposal has been validated
on a real world problem in smart cities (not on an academic benchmark): the optimization of vehicular ad hoc network (VANETs) communications. We make our algorithm to
find an efficient parameter setting of the Ad hoc On Demand Distance Vector (AODV)
routing protocol. Our pSMPSO will optimize two conflicting quality-of-service goals:
maximize the packet delivery ratio (PDR) and minimize the end-to-end delay (E2ED).
The evaluation of every solution requires its simulation with real maps and traffic, what
needs long running times (around 46 s.). The experimental validation of the proposed
pSMPSO is performed by analyzing 15 independent runs of two multi-thread variants
named pSMPSO-8 and pSMPSO-16. They are implementations with 8/16 particles in
the swarm (8/16 threads). The average run time of executing pSMPSO-8 and pSMPSO16 in a single processor is 7,773.62 and 11,603.73 m., respectively. Their multithreaded
versions on a parallel computer reduce those times to 578.76 and 584.87 m., respectively.
Thus, the average speedup is 6.88 for pSMPSO-8 and 13.43 for pSMPSO-16, showing a
desirable almost-linear speedup. Also, the average computational efficiency is 85.96% (8
threads) and 83.95% ( 16 threads), really high values. In order to ease decision making
we have selected representative solutions of the Pareto front and analyzed the induced
behavior in communications of the real scenario. Compared to the standard RFC 3561
and to the optimized configuration got by a sequential and parallel PSO we can confirm
that our pSMPSO outperforms the rest in terms of PDR and E2ED. Moreover, these
improvements increase with the number of vehicles and the communications needs, i.e.
they do scale to real cities.
Keywords: Data Routing, Parallel Multi-Objective Optimization, Software Protocol,
Vehicular Ad Hoc Networks (VANET).
295
WE2G (invited)
Pareto Equilibria in Cournot Competition Under
Uncertainty
Caraballo, M. Angeles (*)
Buitrago, Eva
Monroy, Luisa
In this paper we investigate a competitive situation in which two firms producing a homogeneous commodity have to decide the quantity to offer. There is uncertainty on the
price the commodity will reach in the market since different future scenarios are possible
and it is assumed that no information about the probability distribution of occurrence
of the scenarios is available. This decision-making situation is formalized as a game with
incomplete preferences represented by vector-valued utility functions for which the notion
of Pareto equilibrium is adopted as a natural extension of that of Cournot equilibrium.
Under standard assumptions about the demand curve, we characterize the complete set
of Pareto equilibria. In the second part of the paper we analyse the equilibria to which
the agents will arrive depending on their risk attitudes. We find that equilibria always
exist if both agents are simultaneously either optimistic or pessimistic. However, this is
not the case when each of the agents exhibits a different and extreme attitude towards risk.
Keywords: Cournot Competition, Pareto Equilibria, Strategic Game.
296
WE2D (invited)
Plaster Waste Destination Problem in the Buildings
Sites: An Application of the VFT Methodology
Hazin, Luciana (*)
Alencar, Marcelo
Mota, Caroline
It is each time greater the interest to find solutions that attends the interested parties
and, at the same time, that decrease the impact in the environment originated by the
plaster wasted, that is a material which is widely used in Brazil on construction sites.
With the view to the growing concern with the environment, it becomes each time more
important the understanding of the need of reducing the amount of residue generated in
new works, reducing the losses, and of educating the society in the sense of recycling the
dump generated in the reforms and demolitions. In this sense, the value focus thinking
methodology (VFT), that is respect to how the values can be used to improve the decision making process, was applied. The problem was structured and the parties involved
could get a better understanding regarding their objectives and the consequences for the
decision problem.
Keywords: Plaster Waste, Structuring Decisions, Value Focus Thinking.
297
TU4G (contributed)
Portfolio Multiple Criteria Analysis, Ranking and
Optimal Selection
Fulga, Cristinca (*)
Bucharest University of Economic Studies
In this paper we develop a portfolio selection method in the Mean-Risk framework. The
recent positive evolution of the risky assets that are not comprised in the portfolio available at the moment of the decision is analyzed by using the Analytical Hierarchy Process.
Our model relies on a quantile based risk measure defined using the modified loss distribution according to the decision maker’s risk and loss aversion preferences. We investigate
the practical performance of the proposed model on a portfolio composed of some of the
most representative securities of the New York Stock Exchange.
Keywords: Multi-Criteria Decision Making, Portfolio Optimization.
298
TU4G (invited)
Practical Measurement of Environmental and Social
Achievement Levels for Mutual Funds: A Synthetic
Indicator with its Software Application
Pla Santamaria, David (*) Bravo Sellés, Milagros
Pérez-Gladish, Blanca
This paper has the following purposes: (i) to measure in an objective way the environmental and social levels of achievement for financial assets and mutual funds from multiple
criteria of socially responsible investment; (ii) to provide a software application, which
allows the fund managers to easily implement the proposed ethical measurement. Purpose (i) requires constructing a synthetic indicator, which does not depend on particular
preferences of fund managers, since ethical preferences of investors in mutual funds widely
change from an investor to another. Accordingly, the synthetic indicator is constructed
from domination analysis and decision making analysis under uncertainty. A veto against
the dominated alternatives is proposed. Purpose (ii) requires constructing a specific tool
(the software application), whose framework and practical use are shown. An actual case
from a large number of criteria is developed and commented.
Keywords: Environmental Decision Making, Equity Mutual Funds, Social Decision Making, Socially Responsible Investment, Synthetic indicators.
299
MO5H (poster)
Precise Consistency Consensus Matrix in an
AHP-Group Decision Making Local Context
Moreno-Jiménez, José Mar´ıa (*)
University of Zaragoza
Aguarón, Juan
Escobar, Mar´ıa Teresa
A fundamental factor in multiactor decision making is the possibility of reaching consensus solutions among the actors involved in the resolution process. In general, the
consensus may refer to several aspects of this process, such as, for example, the approach
or technique that is followed, the methodology employed and the procedures used when
aggregating preferences, priorities and judgments. Assuming a multiactor local context
(a unique criterion), analysed with the Analytic Hierarchy Process (AHP) and using the
row geometric mean as the prioritisation procedure, this paper presents a new decisional
tool based on the property of consistency. It improves the behaviour of a previously defined tool (the Consistency Consensus Matrix - CCM) for AHP-Group Decision Making.
This new tool, the Precise Consistency Consensus Matrix (PCCM), allows an automatic
increase in the number of entries in the CCM by identifying precise values in common
consistency intervals. The PCCM results in more informed group decision making and
provides more accurate estimations for the group’s priorities. It can also be used as a
starting point for posterior negotiation processes. The new decisional tool has been applied to a public decision making procedure.
Keywords: Analytic Hierarchy Process, Consistency, Consistency Consensus Matrix,
Group Decisions Making.
300
TU5D (invited)
Price-setting Under Risk Criteria for Virtual Products
and Services
Henig, Mordecai (*)
Tel Aviv University
Avinadav, Tal
Bar Ilan University
Chernonog, Tatyana
Bar Ilan University
This work deals with pricing of “virtual” products, i.e., products that a retailer can supply
after demand has been realized. Such products allow the retailer to avoid holding costs
and ensure timely fulfillment of demand with no risk of shortage. Assuming that demand
is price-dependent and uncertain, we seek to optimize each of three criteria: expected
profit, the likelihood of achieving a profit target, and a profit percentile criterion. Simultaneous multiple criteria are also explored. Two cases of demand uncertainty forms are
analyzed: the multiplicative form, where a stochastic dominance is proved so that all the
criteria are optimized simultaneously; and the additive form, where stochastic dominance
cannot occur. A two-echelon supply chain (comprising both the supplier and the retailer)
is analyzed to show that in the multiplicative form of demand, stochastic dominance occurs in both echelons.
Keywords: Multi-Criteria Decision Analysis, Pricing, Revenue Management, Risk Analysis, Supply Chain Management.
301
TH3D (contributed)
Primitive and Derived Information in Decision Models
Tsoukiàs, Alexis (*)
LAMSADE - CNRS
Colorni, Alberto
Politecnico di Milano
The paper presents a general framework describing decision models. We specially focus
on the so-called primitives of the model, that is information that cannot be derived, but
needs to be learned from preference statements provided by the decision maker. We classify preference statements and what can be induced from them. We then show what
derived information can be constructed. The aim of this framework is on the one hand
to show the limited number of independent methods (in the sense of independent characterising primitives) and on the other hand to provide the basis for justifications and
explanations to be added to decision support methods.
Keywords: Decision Analysis, Decision Support, Preference Learning.
302
TU3B (invited)
Prioritizing Third Party Logistics Providers: An
Analytical Network Process Approach
Karpak, Birsen (*)
Bayazit, Ozden
Central Washington University
As shippers worldwide are increasing their use of third party logistics (3PL) services, the
need for carefully selecting 3PLs is becoming much more critical. Choosing the right 3PL
provider is often a dilemma faced by supply chain managers. Our purpose is to show how
the Analytic Network Process (ANP) can be used in aiding companies with the decision of
selecting the most capable third party logistics provider for an aerospace company. The
aim of this research is to investigate a case of 3PL provider selection decision analysis
using ANP along with a sensitivity analysis to demonstrate the effectiveness of the proposed approach. Data for the study will be collected from an aerospace company located
in Western Washington. We are hoping that the model we will develop in this study will
help companies evaluate outsourcing logistics services.
Keywords: Analytic Network Process, Third Party Logistic Supplier.
303
MO2D (contributed)
Problems, Objectives and Ecosystem Services in
Protected Areas: A Mediterranean Case Study
Segura, Marina (*)
de Castro Pardo, Mónica
Ginestar, Concepcion
Management of protected areas should mainly be addressed using Multiple Criteria and
Group Decision Making approaches. We need to take into account the multiple conflicting objectives and the preferences of all of the stakeholders involved. The first step when
applying MCDM to the management of protected areas is to identify the decision makers
and the stakeholders, the main strategic problems and objectives, as well as their priorities for each group of stakeholders. The objective of this work is to elicit and analyse
the preferences of stakeholders about problems, objectives and ecosystem services in the
network of natural parks in the Comunitat Valenciana, Spain. We have designed and
carried out a survey that has been sent to all 22 Valencian natural parks. Firstly, we
have analysed the global data to define a general framework. The natural parks in this
Mediterranean area can be classified as protected wetlands, forest and marine protected
areas. The nature of problems and conflicts among stakeholders are very different in
each category. Secondly, we focused on two particular parks. One of them is the biggest
forest natural park in the Comunitat Valenciana,Sierra de Espadán, characterized by low
population density and where municipalities are important owners. The second park is a
wetland in a metropolitan area,l’Albufera de Valencia, with high population density and
a lot of groups of stakeholders with a high level of conflict. In this case we have obtained
responses from the park director, staff, and the representatives of municipalities, private
owners, farmers, fishers, hunters, unions, conservation associations and others. We have
also carried out a survey focused on this wetland using the social networks Facebook and
Twitter. Finally, a discussion of results and conclusions will be presented. This work
represents the first phase in implementing a collaborative and multiple decision making
process to solve specific problems in these protected area.
Keywords: Case Study, Decision Maker’s Preferences, Ecosystems, Group Decisions
Making, Identifying Objectives, Multi-Stakeholder Decision Making.
304
WE1C (contributed)
Production Points with the Same Reference
Hyperplane in DEA: Applications in Sensitivity
Analysis and Returns to Scale Classification
Dehnokhalaji, Akram (*)
Aalto University
Nasrabadi, Nasim
Birjand University
Soleimani-Damaneh, Majid
University of Tehran & IPM
In this study, we consider a given supporting hyperplane of the Production Possibility
Set (PPS) denoted by H and obtain a subset of the PPS with the property that for each
production point belonging to that, the radial projection point lies on H when we assess
the production activities by CCR or BCC model in DEA. The results are interesting not
only from the theoretical viewpoint but also from the applied point of view. Theoretically,
we have established some lemmas, theorems and corollaries using linear programing and
DEA techniques to construct a mathematical formulation for the above mentioned subset.
Identifying a set of production activities having the same rate of trade-offs is one application of our study. Also, the theory enables the decision maker to find a preservation
region for preserving the Returns to Scale Classification of the DMUs.
Keywords: Data Envelopment Analysis, Preservation Region, Radial Projection, Reference Hyperplane, Returns To Sclae, Sensitivity Analysis.
305
WE1E (invited)
Promoting Sustainability in Mining: Multicriteria
Assignment of Resources in Vale
Autran M. Gomes, Luiz F. (*)
Ibmec/RJ
Camanho, Roberto
Tipec/SP
Macedo, Marcelo G. C.
Vale/Toronto
Camanho, Thomaz M.
AMPLIX - Consultoria em Excel
Martins, Luiz Geraldo Biagioni
Ibmec/RJ
The mining and metals industry is core to the global economy. Sustainable development
is a multidimensional concept, including socio-economic, ecological, technical and ethical
perspectives. As a consequence, sustainability issues are characterized by a high degree
of conflict. Mining companies, like all businesses, focus on financial viability and profitability. The mining sector’s environmental and social footprint can be significant, with
impacts at local, regional, national and global levels. MCDM is a significant methodology
to aid in getting a comprehensive understanding about the sustainability performance
of mining companies. In this paper we consider as a realistic example the case of the
Brazilian mining company Vale and show how sustainability issues can be tackle in this
company by using the AHP coupled to a linear program for assigning resources. We close
the paper with general guidelines for choosing MCDM analytical methods for promoting
sustainability in the mining sector.
Keywords: Allocation, Analytic Hierarchy Process, Cost Benefit Analysis, Linear Programming, Sustainability.
306
TH2E (invited)
Properties and Benefit of the Linear Efficient Frontier
Approximation in the Objective Space
Maag, Volker (*)
Fraunhofer ITWM
Grebe, Tabea
Fraunhofer ITWM
Nowak, Uwe
Fraunhofer ITWM
Given a practical optimization problem with multiple criteria, a common challenge is to
provide an intuitive, fast and clear interface supporting the decision maker. But at the
same time we want to avoid a high-handed, arbitrary simplification of the objectives. For
convex objective functions we can achieve both by linearly approximating the efficient
frontier in the objective space. This is used to enable the decision maker to ’navigate’ on
the efficient solutions by interpolating the precalculated solutions in real time to satisfy
the decision makers demands as good as possible. This ’navigation approach’ is established and proven to be successful. However, the approximation is unacceptably slow if
the number of objectives is too large. However, in practical application we made an interesting observation: In cases of many objectives one usually needs just a small fraction of
the approximation for doing the navigation and satisfying the decision maker’s demands.
In the talk we present and explain this phenomenon and discuss how to use it to our
benefit. For this we notice that the linear approximation is in fact a polyhedron with
specific geometric properties. The analysis of it helps us to design an extension of the
approximation allowing to apply it to larger number of objectives. The main ingredients
for this are ideas from principal component analysis and singular value decomposition,
respectively.
Keywords: Efficient Frontier, Interactive Methods, Linear Approximation, Multi-Objective Optimization, Non-Linear Multi-Objective Optimization, Principal Component
Analysis, Singular Value Decomposition.
307
FR2G (contributed)
Proximity-Based Decision Rules for Multicriteria
Model Predictive Control Problems
Skulimowski, Andrzej M.J. (*)
AGH UST
The complexity of the problem how to define suitable decision rules for multicriteria
decision-making problems occurring in model predictive control depends strongly on the
nature of additional preference information available during the decision-making process.
A common virtue of such problems is that the relation between the variability of the preference structure, the system parameter changes, and the computational capabilities of the
problem solver play an important role. There also exist trade-offs between different solution approaches: an adequate control system model can yield an accurate solution but it
might be never deployed because of the relatively quick changes in the system parameters
or in the decision-maker’s preferences that exceed the computational and implementation
capacities. Such problems occur e.g. in dynamic decision problems where the system parameters are derived from so-called big data streams or in discrete event control problems,
where observable events are retrieved from the web, a monitoring, or a telecommunication
system. Due to a large amount of data, the model of the system dynamics, the prediction,
and decision rules must be simplified to an extent allowing their efficient processing and
timely deployment of control. This paper will propose a methodology that refers to the
anytime and continual computing paradigms and allows for a gradual simplification of the
model by an appropriate discretization of time and system parameters to comply with
the computational capabilities estimated ex-ante. Then we will show how to simplify the
preference structures and define efficient proximity-based decision rules to cope with the
limited decision-maker’s capability to calculate the solution in due time. This approach
has been applied to recommend Pareto-optimal strategies of information society development described by a discrete-time linear non-stationary control system with parameters
identified based on data from heterogeneous web sources.
Keywords: Anytime Computing, Control, Multi-Criteria Model Predictive Control, Optimal Control, Preference Information, Preference Modeling, Proximity-Based Decision
Rules, Reference Multifunction Method.
308
TH1A (plenary)
Public Decisionmaking and Decision Conferencing
Bana e Costa, Carlos (*)
In this talk, I shall reflect on lessons learned from my professional involvement, as a
decision-analyst and process consultant, in a variety of socio-technical processes, in which
multicriteria value measurement techniques were used during decision conferences, with
the participation of several types of social actors and stakeholders, with the common aim
of facilitating better public decisionmaking.
309
WE1C (contributed)
Quality of the Information Provided by Fuzzy Data in
the Efficiency Evaluation of the Spanish Textile
Industry
Baeza-Sampere, Ismael (*)
Coll-Serrano, Vicente
Efficiency results obtained by applying conventional DEA models are usually used for
Decision Making. These assessments are based on the assumption of certain data and,
as noted by some authors in the academic literature, this assumption is not always acceptable. In these cases it is preferable to consider some kind of imprecision in the data.
This is the aim of Fuzzy DEA models. The importance of the quality of the results is
what leads us to propose a method to evaluate it. The comparison with the results of a
crisp DEA model at the lower and at the upper values in the worst alpha-cut will allow us
assess the quality of the results obtained when evaluating the efficiency of Spanish textile
companies with a possibilistic model.
Keywords: Data Envelopment Analysis, Fuzzy Mathematical Programming, Possibilistic Model.
310
MO3D (contributed)
Quantifying Public Preferences for Evaluation
Multifunctional Agriculture System, using Analytic
Hierarchy Process
Marqués, Inmaculada (*) Pérez-Salas, José Lu´ıs
Segura Garc´ıa del R´ıo, Baldomero
Some agricultural systems in the urban fringe, maintain a fragile equilibrium between the
consideration of agricultural space and/or periurban areas, subject to pressure by alternative use of the land (industrial, infrastructure, commercial and leisure centers, ..). Some
of these agricultural systems are characterized as agricultural ecosystems that provide
goods and services related to leisure and recreation, the process development beneficial
to the environment, such as fixing CO2, the production of healthy and safe food, the
preservation of natural and cultural heritage, ... The production of these goods and services derives mainly from practicing agriculture itself, and is generated simultaneously
with the production of appropriate market goods. These are goods which do not have
a market where farmers could sell them. The lack of de market can justifies the intervention of public administration. The difficult we are facing is the correct estimate of
the social demand for these goods and services. An efficient policy should be attempt
the public preference for non-market goods and services, and should be paid attention
to the explicit measurement of public demand. Analytical Hierarchy Process can help
assess the social priorities to integrate them into the decisions policies with the aimed to
maximize the use of the agricultural system. In this paper we study social preferences
for the functions of the land system of the Valencia urban fringe. We take representative
residents of the study area and compare them with stakeholders. We must show, how
far the preferences of the stakeholders are truly representative for the community as a
whole. In both cases the priorities are calculated, and the aggregation of these is studied
taking in to consideration the consistency of the individuals by adrresing their opinion
of the preferences, on individual and aggregated level, studying the relation between them.
Keywords: Agricultural Public Goods, Agriculture, Consistency, Decision Maker’s Preferences.
311
WE2B (contributed)
Rank Reversal in the PROMETHEE Methods: a
Summary of Recent Investigations
De Smet, Yves (*)
Université libre de Bruxelles
Multicriteria methods based on pairwise comparisons suffer from possible rank reversal
occurrences when the set of alternatives is modified. Evidences of this distinctive feature
have been found in AHP, ELECTRE and PROMETHEE. In this talk, we summarize the
results of four related works done in the context of the PROMETHEE I and II methods
(Mareschal, B. et al. (2008); Roland, J. et al. (2011); Verly, C. and De Smet, Y. (2013);
Eppe, S. and De Smet, Y. (forthcoming)). First empirical tests are conducted on the
basis of artificial data sets in order to quantify the likelihood of rank reversal instances.
Then extensions to real data sets are presented. Conditions to avoid this outcome are
studied. This leads us to develop theoretical bounds between flow scores differences that
allow the detection of potential rank reversals. Then, extensions of the classic definition
of rank reversal are considered; deletion of a non-discriminating criterion, addition of a
copy of an alternative, etc. Finally, we investigate risks of manipulation on a simplified
version of the PROMETHEE II ranking.
Keywords: PROMETHEE, Rank Reversal.
312
TH2C (contributed)
Ranking Candidates through Variable Convex
Sequences of Weights
Llamazares, Bonifacio (*)
University of Valladolid
Scoring rules are a well-known class of positional voting systems where fixed scores are
assigned to the different ranks. Nevertheless, since the winners may change according to
the scores used, the choice of the scoring vector is not obvious. For this reason, and by
using the DEA methodology, several methods have been suggested so that each candidate
may be evaluated with the most favorable scoring vector for him/her. In this work we
propose a new positional voting system that allows to use different scoring vector for each
candidate and avoid some shortcomings of the methods proposed in the literature. For
this, we use constraints that assure convex sequences of weights.
Keywords: Convex Sequences Of Weights, Data Envelopment Analysis, Positional Voting Systems, Scoring Rules.
313
TH2C (contributed)
Re-Measuring the Happy Planet Index using DEA and
MCDM Models
Jablonsky, Josef (*)
University of Economics Prague
Happy Planet Index (HPI) is an aggregated index that measures the extent to which each
nation produces long and happy lives per unit of environmental input. The HPI uses
global data on life expectancy, experienced well-being, and ecological footprint in order
to rank countries. The last HPI report was published in 2012 and it contains data for 151
countries of all continents. The aim of the paper is to re-calculate the HPI using DEA
models and other multiple criteria decision making techniques and compare the given
results. In addition the formulation of the problem is extended by some other criteria
(GDP per capita, index of governance, etc.) and analyzes the impact of the new indicators on final ranking. MCDM methods evaluate alternatives (countries) according to the
given set of criteria with respecting preferences of decision makers. Most of them allow
ranking of alternatives according to aggregated indices defined by the methods (WSA,
PROMETHEE II, TOPSIS, AHP, etc.). DEA models compare the countries with the
best performers in the data set and measure the efficiency of transformation of multiple
inputs into multiple outputs. Even they are based on different principles than MCDM
methods they allow ranking of evaluated units according to their efficiency expressed by
efficiency or super-efficiency scores. The paper analyzes both methodological approaches
and compares their results. Numerical experiments are realized using original MS Excel
add-ins for DEA models (DEA Excel solver) and MCDM methods (Sanna). Both applications can be downloaded from author’s web pages.
Keywords: Data Envelopment Analysis, Multi-Criteria Decision Making, Software.
314
TU4F (contributed)
Redundant Phenotype-Objective Space Mappings in
Multi-Criterion Data-Clustering
Handl, Julia (*)
John, Agah
Niger Delta University
Knowles, Joshua
Delattre and Hansen (1980) propose the definition of data clustering as a bicriterion optimization problem and describe an exact approach to bicriterion clustering, which uses
branch-and-bound in combination with graph colouring to obtain the Pareto front for the
minimization of maximum diameter and the maximization of minimum split. Due to limited computational resources at the time, the evaluation of the approach was limited to a
handful of data sets of limited size. More recently, multiobjective evolutionary algorithms
(MOEAs) have been used to provide more general implementations of multiobjective data
clustering, which allow for the optimization of arbitrary clustering objectives (Handl and
Knowles, 2007). Here, we are interested in a direct comparison of Delattre and Hansen’s
method with an MOEA optimizing the same pair of objectives. Our comparison focuses
on the objective values obtained as well as the external validity of the results obtained.
An analysis in objective space shows that - as may be expected - the MOEA produces
approximations to the Pareto front only, and that some of its solutions are dominated
by those partitionings that are generated using the exact approach. On the other hand,
the external validation of clustering solutions reveals a performance advantage for the
MOEA, which seems counter-intuitive at first. Further investigation reveals that this
finding arises from redundant phenotype-objective space mapping in certain formulations
of data clustering. In particular, the concrete mathematical form of the minimum diameter and maximum split means that the search space of possible partitionings contains
sets of different clustering solutions that project to the same points in objective space.
The use of a linkage-based encoding in the MOEA (Handl and Knowles, 2007) implicitly
deals with this redundancy by introducing an implicit bias towards high-density clusters.
Keywords: Evolutionary Computations, Evolutionary Multi-Objective Optimization,
Multi-Criteria Decision Analysis.
315
TU3E (contributed)
Reference Point Methods and Approximation of
Pareto Sets
Goetzmann, Kai-Simon (*)
TU Berlin
Matuschke, Jannik
TU Berlin
B¨
using, Christina
RWTH Aachen
Stiller, Sebastian
TU Berlin
In theoretical computer science, research on multicriteria optimization problems usually
revolves around the set of Pareto optimal solutions, or, for reasons of tractability, approximations of this set. In practical MCDM, on the other hand, methods that output
a single solution are more widespread. Among these methods, reference point solutions
and, as a special case, compromise programming, are especially popular. A reference
point solution is the solution closest to a given reference point in the objective space.
Compromise programming uses the ideal point as a reference point. We establish a link
between reference point methods and the theory of approximate Pareto sets, by showing a
polynomial equivalence of approximability: An approximate Pareto set can be efficiently
constructed, if and only if there is an efficient algorithm that computes a reference point
solution with a provable approximation guarantee. To illustrate our results, we lift some
approximation techniques from single-criteria optimization to reference point methods,
resulting in approximation algorithms for the Pareto sets of the multicriteria versions of
some classical combinatorial optimization problems.
Keywords: Achievement Scalarizing Functions, Combinatorial Optimization, Compromise Programming, Provable Approximation Guarantees, Reference Point.
316
TU2B (contributed)
Relational Multi-Attribute Models in DEX
Methodology
Nejc, Trdin (*)
Jozef Stefan Institute
Bohanec, Marko
DEX is a qualitative decision support methodology aimed at evaluation and analysis of
decision alternatives. The method is based on hierarchical decomposition of attributes,
where leaf nodes of the hierarchy represent the input attributes of the alternative and
the root attribute represents the final evaluation of a particular alternative. Furthermore, other intermediate attributes of the hierarchy represent logical combinations of
lower attributes into higher level concepts. The aggregation in DEX models is governed
by qualitative combinatorial tables, easily understandable as if-then rules. Many decision problems addressed in real life applications are based on some relational properties
between at least two types of entities. For example, when evaluating a company, many
departments of that company must be evaluated, too. The number of departments may
vary in time, but each of the departments contributes to the evaluation of the whole
company. Similar, but more complex example of relational properties is when assessing
the reputational risk of a bank. Banks represent many counterparts, which serve many
different products. These products are bought by several bank’s clients. The assessment
of reputational risk thus depends on many relational connections. Currently, DEX only
supports the evaluation of non-relational (“flat”) alternatives. In order to address relational problems, ad-hoc manual or programmatic manipulation of models is necessary,
which adds additional burden to solving such problems. In this work we address the
task of extending the multi-criteria decision making method DEX to relational decision
problems. We will present a formal description of DEX and extend it to facilitate the
treatment of relational decision alternatives. We will explain and justify the need for
such an extension arising from practical applications and illustrate the use of the newly
extended methodology on a real life example.
Keywords: Preference Aggregation, Qualitative Multi-Criteria Model, Relational Modeling.
317
FR3F (contributed)
Rewarding Decisions Beyond the Performance Targets
Paradigm
Sousa, Victor (*)
Universidade do Porto
Faustino, Ana
Universidade do Porto
A water resources management model was first approached using MCDM techniques in
order to involve the representatives of the several uses of available water (in reservoirs
or underground aquifers) in the design of rules to carry out that management. We were
successful in having a model of optimization ruling management decisions, or “driving
the system”, which was compatible with the stochastic process of inflows to the system
and used, in a particular way, dynamic programming and multicriteria analysis. This
model worked under the establishment of independent performance targets and penalty
functions for each selected use of water. The optimization DP through a given elapsed
time was made according to previous accepted rules settling those targets through all
different seasons and specific conditions. The optimal solution provided a driving policy of the reservoir by minimizing the global penalties for the accumulated shortages, as
agreed with the representatives of the different users of water. This “performance targets
paradigm” has been questioned by a few water resources specialists and an alternative
was presented by Abbas and Matheson, 2009, mainly by introducing and developing the
concept of “value aspiration equivalent” to overcome the artificial independence of targets and regulate trade-offs among the performance objectives. Our first adjustment of
our model inspired by this new paradigm led to the consideration of “reward functions”
along the “penalty functions”, those set of functions unveiling a good architecture to the
trading-off system. Results were very similar to the first model under shortage situations, but provided non negligible advantages under sufficiency of resources. Our most
recent work proposes a methodology to merge those two types of functions along with
optimisation techniques, obtaining interesting results either under shortage or abundance
of resources.
Keywords: Penalty And Reward Functions, Performance Targets, Water Resources.
318
FR3F (contributed)
Risk Assessment of Large Scale Hydroelectric Plant
Projects using Fuzzy Analytical Hierarchy Process
Ribas, Jose Roberto (*)
Delta Energia
Sohler, Flavio
Furnas Centrais Eletricas S/A
The difference between planned and actual outcomes of a project is associated to risk
events, in order to avoid poor performance; the construction industry is required to manage such risks. Research and practice has shown that construction of power plants embeds
many risks. This study presents a method that integrates the expert subjective opinion
and a multicriteria method, aiming at setting up hierarchical risk event structures related to the most relevant service packs of the project. Such information was captured
by means of in-depth interviews and content analysis. The strength of one event risk
against the other was assessed through pairwise comparisons using a nine-point Saaty’s
scale. The adjustment for low precision of subjective opinion and the quantitative assessment of the event risks weights were achieved by means of a fuzzy analytical hierarchy
process (FAHP). The risks associated to Santo Antonio hydroelectric plant were assessed.
This plant, which is located in the Amazon rainforest region, has 3,580 MW of installed
capacity, and represents one of the main challenges to the Brazilian engineering. Mainly
because it will work with bulb turbine technology and will generate energy from the flow
of the river Madeira itself, without the need of creating a large reservoir. This complex
large scale project has five major event risk-related to five service packs, as specified by
the experts. A comparison was made between the risk events weights estimated through
the FAHP technique for two segments of participants: the construction managers and the
owner’s engineers. It was concluded that some of the differences were due to the opposing
risk profiles of the two groups, as a consequence of their obligations as established by a
turnkey lump sum contract.
Keywords: FAHP, Hydroelectric Plant, Risk Analysis.
319
FR3D (invited)
Risk-Averse Bi-Objective Optimization via Stochastic
Dominance under Transaction Costs
Gutjahr, Walter (*)
University of Vienna
A common approach to bi-objective stochastic optimization determines the set of “expected value efficient solutions” (Pareto optima w.r.t. the expectations of the objectives).
However, this consideration presupposes a risk-neutral decision maker. In the presence
of risk aversion, an analysis becomes much more complex. That is why authors as Caballero et al. (2004) questioned the suitability of the “multi-objective method” in this
context at all. The talk proposes an alternative approach based on the concept of multivariate stochastic dominance constraints, as it has recently been elaborated to address
risk aversion w.r.t. more than one outcome variable. This concept uses a benchmark
solution and optimizes an objective function over all solutions stochastically dominating
the benchmark. Whereas the most common multivariate stochastic order in the stochastic
programming literature, the positive linear stochastic order, implicitly assumes a perfect
market allowing to trade objective outcomes against each other, the present talk introduces a class of stochastic dominance relations based on the more realistic assumption
that trading is only possible under transaction costs. The introduced class of dominance
relations can be shown to contain both the positive linear order and the so-called component order as boundary cases. The variant for linear transaction costs is then applied
to the special case of separable, piecewise linear, nondecreasing and concave utility functions. Mathematical programming formulations are derived. Finally, the application of
the approach is outlined for a bi-objective stochastic covering location problem. For the
computational solution, exact methods reyling on branch-and-bound as well as multiobjective evolutionary metaheuristics as NSGA-II are discussed. For a small real-world
disaster management instance analyzed in a previous publication under risk neutrality,
the exact Pareto frontier is determined in the risk-averse framework.
Keywords: Covering Location Problem, Multi-Objective Optimization, Stochastic Dominance Constraints, Stochastic Programming, Transaction Costs.
320
FR2C (invited)
Robust Elicitation of a Qualitative Ranking Model
using Inconsistent Data
Ouerdane, Wassila (*)
Ecole Central Paris
Liu, Jinyan
Mousseau, Vincent
Ranking with Multiple reference Points, namely RMP method is a family of qualitative
aggregation models for multi-criteria decision aid (MCDA). In this work, we are more
concerned about a Simplified RMP model (terms to S-RMP), which is an outranking
method towards ranking problem in MCDA. The aggregation proceeds by decomposing
additively the importance relation on the set of criteria and it takes into account a lexicographic order of dictator reference points. A S-RMP model can be elicited from pairwise comparisons provided by the Decision Maker (DM). However, the current S-RMP
elicitation algorithms are not designed to consider specifically the situations where the
information provided by the DM contains inconsistencies. To tackle this problem, we
propose a new version of the elicitation algorithm model by processing the inconsistency
of pairwise comparison statements under the prudence principle. This new elicitation
algorithm is formulated by a mixed integer linear problem. To test and evaluate the
new algorithm’s performance, we implement the different elicitation algorithms for SRMP model in Java and solve the problem with CPLEX Optimizer. In addition, the
programs have been structured into several modules to achieve various functions and the
Input/Output is compatible with the XMCDA data standard defined via a XML schema.
We design a series of numerical experiments for the new algorithm. The empirical results
show that the new elicitation algorithm for S-RMP model has a greater ability to deal
with the inconsistencies. For future research, we plan to extend this work to the case
in which multiple decision makers provide pairwise comparisons; indeed it is similar to
the inconsistency problem in that the multiple DMs may provide conflicting preference
statements.
Keywords: Inconsistency, Multi-Criteria Decision Aid, Pairwise Comparison, Reference
Point, Robust Elicitation.
321
FR2C (invited)
Robust Ordinal Regression in Geographical
Information Systems
Greco, Salvatore (*) Corrente, Salvatore
Boggia, Antonio Massei, Gianluca
University of Perugia
Slowinski, Roman
Multicriteria Spatial Decision Support Systems (MC-SDSS) couple Geographic Information Systems (GIS) and Multicriteria Decision Aiding (MCDA). On the part of MCDA, we
propose to use Robust Ordinal Regression (ROR) methodology. ROR considers the whole
set of utility functions that are compatible with the preference information provided by
the Decision Maker (DM). With this aim, ROR presents the necessary preference, which
holds when an alternative a is at least as good as another alternative b for all compatible
utility functions, and the possible preference, which holds when an alternative a is at least
as good as another alternative b for at least one compatible utility function. ROR has
been adapted to deal with ranking and sorting problems, and it can also be used when
preferences are represented by an outranking relation. Using ROR one can also analyze
the possible rank of the considered alternative through the so-called Extreme Ranking
Analysis. ROR permits also to consider interactions among criteria using either the Choquet integral or an “enriched” additive utility function including positive and negative
synergy components. We discuss the benefits of application of ROR to MC-SDSS, and
we illustrate this application by a case study on land use.
Keywords: GIS, Multi-Criteria Decision Aid, Robust Ordinal Regression.
322
TU2F (contributed)
Robust Selection of Environmental Quality Metrics
based on a Multi-Criteria Pareto Data Mining Process
Udias, Angel (*)
Joint Research Centre
Galbiati, Lorenzo
Agència Catalana de l’Aigua
Cano, Javier Chiazzaro, Mauricio
Rey Juan Carlos University
Redchuk, Andrés
Universidad Autónoma de Chile
Sustainability of water resources has become a challenging problem worldwide. In this
context, the development and application of new political and management strategies,
aimed at overcoming the growing problem of scarcity of fresh water, is of vital importance. Specifically, planning and management of water resources requires making decisions characterized by multiple objectives and stakeholder groups, according to various
criteria measuring water quality. This would typically imply deciding what action should
be accomplished at each of a series of spatial locations along the river, as well as their
effects on such criteria. Hence, these problems, called Spatial-MCDM methods, extend
traditional multicriteria decision methods by including explicit geographic information
about the alternatives and quality objectives. However, Spatial-MCDM methods are not
universally accepted as a standard way to assess the quality of a river, since, for a specific
indicator, different techniques for spatial data aggregation are conceivable, possibly yielding different tradeoffs and decision strategies. Throughout an aggregation process, the
original spatial data are reduced to a small number of data elements. Moreover, the aggregation process may alter the statistical and spatial characteristics of the data. To study
these effects, we apply different aggregation techniques on a catchment scenario within
a hydrological MCDM methodology. Such procedure is aimed at finding an Program of
Measures (PoMs), fulfilling the EU Water Framework Directive objectives, and consisting
of: 1) A water quality model (to simulate the effects of the PoMs); 2) A Multi-Objective
Evolutionary Algorithm, used to identify efficient trade-offs between PoMs’ costs and water quality; and 3) A data mining process of the Pareto set, in order to extract knowledge
from Pareto-optimal decisions. A comparison using different aggregation techniques is
presented, showing which metrics yield more robust results.
Keywords: Data Mining, Environmental Decision Making, Evolutionary Multi-Objective
Optimization, Multi-Objective Optimization, Pareto Optimal Solutions, Spatial MultiCriteria Evaluation, Water Resources Management.
323
WE1G (contributed)
Robust Weighted Solutions for Multi-Objective Linear
Programming
Ogryczak, Wlodzimierz (*)
Warsaw University of Technology
In Multi-Objective Linear Programming (MOLP) any efficient solution can be found by
the weighting approach with some positive weights allocated to several objectives. The
weights settings represent preferences model thus involving impreciseness and uncertainties. The resulting weighted average performance may be lower than expected. Several
approaches have been developed to deal with uncertain or imprecise data. We analyze
robust approaches to the weighted averages of objectives where the weights are known
imprecisely. For the case of unlimited weights selection the worst case weighted average
becomes the worst outcome (max-min solution). In the case of box impreciseness, the
worst case weighted average is a parametric generalization of the worst outcome to the
so-called tail average. We show that while considering robust models allowing the weights
to vary only within given intervals, the tail average represents the robust solution for only
upper bounded weights. For any arbitrary intervals of weights the corresponding robust
solution may be expressed by the optimization of appropriately combined weighted average and tail average of objectives thus remaining easily implementable with auxiliary
linear inequalities.
Keywords: Linear Programming, Multi-Objective Optimization, Robustness And Sensitivity Analysis.
324
MO5H (poster)
Routing System Optimization – Case Study
Fuentes Rojas, Ever Angel (*)
Universidad Libre
Mart´ınez Gamboa, Jeyson Andres
Universidad Libre
The requirements demanded by the society make companies optimize its distribution processes as an improvement strategy in order to be competitive and survive in the market for
satisfying customers’ needs. Plastics industry is no stranger to this situation that is why
it is necessary to optimize the routing system of a company type, which has its activity
center in Puente Aranda, Bogota- Colombia, and has decided to reserve its business name
for this research. It has as starting point the results that were got in the diagnosis test.
Thereafter data were analyzed through the use of administrative tools as: Cause- effect
diagram, Pareto, ABC classification and SWOT matrix. These ones allowed establishing
the most important variables: Costs, distance and response time. This form is achieved
to optimize the routing system by the application of the mathematical model of the travel
salesman problem (TSP) whose solution was found through the use of programs as Gusek
and Grafos. These programs offer an optimal solution with costs reduced 32% and service
level increased 26%. Prospective system is implementing through the use of 3 scenarios
which were tested with simulation techniques in order to select a decision. Finally financial indicators serve as support of the decisions made by the company representing the
best course of action to follow with lower costs and good percentages in the internal rate
of return (IRR) of the investments.
Keywords: Costs, Graph Theory, Optimization, Plastic, Travelling Salesman.
325
FR2F (invited)
Satisfying Multiple Objectives in Wastewater
Treatment Network Design Using a Diversity
Preserving Evolutionary Algorithm
Chmielewski, Hana (*)
Ranjithan, S. Ranji
There is a growing viewpoint in multi-objective civil infrastructure modeling that the
success of an optimization method depends on its ability to handle unmodeled as well as
modeled objectives. Solutions that are optimal in one or more objectives may not be feasible, or reflect unmodeled priorities. Using a diversity-preserving evolutionary algorithm
(EA) is a step towards increasing algorithm robustness in producing ?nal solution sets
that are more likely to contain diverse, high-quality alternatives that address the modeled
and unmodeled issues facing decisionmakers. A regional wastewater network case study
in DuPage County, Illinois, can be modeled to exhibit the cost, environmental, and resilience tradeoffs experienced in wastewater management. The case study area includes
wastewater sources, potential treatment plant locations, and possible interceptor routes
between sites. In addition to minimizing costs, a newly-defined network decentralization
objective is added to carry out a two-objective optimization. The diversity of the decision
space is explored using two approaches: a new diversity preserving EA approach called Diversity Ranking Evolutionary Multi-objective Algorithm (DREMA), and a mixed-integer
linear programming (MILP) approach, both designed to find solutions that are maximally
different in the decision space. To simulate decision-making scenarios in which new priorities may arise mid-analysis, the performance of the two-objective results of DREMA
and MILP are compared against several unmodeled issues, including a third network resilience objective. Compared to MILP, DREMA produces solutions competitive in cost,
and competitive or superior in the decentralization objective and resilience performance.
Both methods produce highly diverse solution sets, as measured by two diversity metrics,
and DREMA does so without the inconvenience of objective function reformulation after
each alternative.
Keywords: Decision Space Diversity, Evolutionary Computations, Multi-Objective Optimization, Network Resilience, Water Resources Management.
326
TU5D (invited)
Scenario-based Multi-Criteria Decision Support for
Robust Humanitarian Relief Supply Chains
Comes, Tina (*)
University of Agder
Schätter, Frank
Humanitarian relief logistics are among the most challenging problems in logistics and supply chain management. We present an approach combining decision-analytical techniques,
optimization and scenario-based reasoning and demonstrate our results by a case-study
(Haiti earthquake 2010). Our approach focuses on a facility location problem (FLP), a
strategic decision problem, which is essential for designing supply chain networks . Risks
in an environment hit by a disaster are hard to assess. The environment is characterized by
high complexity and uncertainties, and information is sparse or even lacking particularly
in the early phases of the disaster. As typically, various organizations need to collaborate,
multiple objectives and preferences need to be taken into account to facilitate consensus
building and coordination. In these situations, techniques from Multi-Attribute DecisionMaking (MADM) have proven useful. These assume that a small set of alternatives has
been identified. Typically, these alternatives are created intuitively, without relying on
computational support, as decision makers need to respond quickly under stress and time
pressure. We present a systematic way to generate and evaluate alternatives to support
the design of robust relief supply chains by combining optimization and MADM. Scenarios
are used to explore different developments of the disaster situation in a dynamic manner.
Fundamental uncertainties after the Haiti earthquake comprise the disaster’s impact on
the population, infrastructure , available resources, or damages and disruptions. These
most prominent uncertainties are captured in a set of scenarios and are evaluated by
measures of MADM. The use of a scenario-based MADM combined with techniques from
operations research to solve the FLP enables identifying robust alternatives (in terms of
stability and quality) that perform well under various scenarios and thus enable coping
with fundamentally different situation developments.
Keywords: Facility Location Problem, Humanitarian Logistics, Multi-Criteria Decision
Aid, Robust Supply Chains, Scenarios.
327
TU5G (invited)
Searching Graphs with Lexicographic Goal References
Mandow, Lorenzo (*)
Pérez-de-la-Cruz, José L.
Pulido, Francisco Javier
Universidad de Malaga
We consider the problem of searching for paths in a multiobjective graph from given
source and destination nodes. More precisely, we search for the subset of all Paretooptimal paths that satisfy a set of lexicographic goals, i.e. goals grouped in preemptive
priority levels. We assume additive costs, and minimize the weighted deviation of goals
for each priority level. We propose LEXGO*, a new algorithm for the efficient solution
of this problem. LEXGO* is a label-setting algorithm that generates partial paths from
the start node to the destination. Each partial path is in turn expanded, filtered, or
pruned. Conditions for filtering and prunning are defined in such a way that discarded
paths are among those that would never generate an optimal solution. Hence, it can be
proved that LEXGO* is admissible (i. e., it finds exactly the set of best solutions). The
algorithm can exploit heuristic information to improve search efficiency. It can be also
proved that, under certain conditions, the number of explored labels can be reduced using
better heuristics. Some experiments have been carried out to analyze the performance of
LEXGO*. Random problem have been generated on a grid and solved (i) by LEXGO*;
(ii) by the optimal multiobjective algorithm NAMOA*, first generating all Pareto-optimal
solutions and then selecting among them those that satisfy the goals. Experiments show
that: (a) when few solutions exist, LEXGO* achieves a substantial reduction of execution
time over procedure (ii); (b) when many solutions exist, execution times are very similar.
Keywords: Artificial Intelligence, Goal Programming, Multi-Objective Optimization,
Pareto Optimal Solutions.
328
MO4G (contributed)
Sensitivity and Efficiency of Multiple Criteria
Alternatives of the Model ’Revenue - Risk’
Sarkisyan, Rafael (*)
Moscow University of Engineering
Eskova, Olga
Employing a multiple criteria logic for the analysis of investment solutions on the basis
of the model “Revenue - Risk” allows to achieve an acceptable compromise between the
competing values of revenue and risk criteria with regard for the key economic factors of
a situation and its development trends. The strategy of a multiple criteria optimization is
based on the concept of preferences and utility, a consecutive revelation and description
of which in the framework of the friendly interactive procedure serves an effective means
for reducing the uncertainty and non-comparableness of alternatives. With regard to the
model “Revenue - Risk” it allows to efficiently combine scales for measuring the revenue
and risk into a significant scale for measuring the degree of their domination.
The transformation of the initial space of estimates R0 = [0, ρp ] × [0, rp ], where ρp and
rp are potentially achievable values of the risk ρ and the revenue r respectively, into the
space F0 = {f ∈ E+2 /f = (f1 , f2 )T , f1 = ρp − ρ, f2 = r} with the coordinated directions
of preferences increase, allows to bring in as the main tools of analysis and optimization
the measures of sensitivity σ(f ) = f T ∇u(f )/u(f ) and of efficiency (or return) ν(f ) =
f T ∇u(f ) = σ(f )u(f ) of the concave increasing on F0 differentiable utility function u(f ).
Investigation of the behavior of the functions ν(f ) and σ(f ) on the indifference curve
u(f ) = const, generates the perspective estimates trajectory f c (λ) with the maximum
values of ν(f c ) and σ(f c ) and with the level of the coordinates interaction function ϕ(f c ) =
−f cT Hf c = (1 − λ)ν(f c ); λ ≤ 1 is the Lagrange multiplier, H is the Hessian matrix of
the function u(f ).
A construction and interpretation of these correlations on specific numerical data are illustrated on the example of the quadratic approximation u(f ) = aT f + (1/2)f T Hf .
Keywords: Measures Of Sensitivity And Efficiency, Multi-Criteria Logic, Preferences
And Utilities, Revenue-Risk Model.
329
WE1D (contributed)
Simulation-Based Innovization for Improving
Productivity and Energy Efficiency of Manufacturing
Systems
Ng, Amos (*)
Goienetxea, Ainhoa Urenda Moris, Matias
Deb, Kalyanmoy
Dudas, Catarina
Volvo Group Trucks Technology
This paper introduces an extended simulation-based innovization (SBI) methodology
which combines the use of discrete-event simulation with evolutionary multi-objective
optimization (EMO), multi-criteria decision making, data mining and advanced data
visualization for eliciting knowledge for intelligent optimization and informed decision
making in systems engineering problems. Special focus will be paid on how the methodology can be applied to simultaneously improve the productivity and energy efficiency
of manufacturing systems producing discrete products, illustrated through several realworld case studies conducted in automotive industry. The SBI methodology is based
on the innovization procedure, originally introduced for unveiling new and innovative
design principles in engineering design problems. Although the innovization method is
based on multi-objective optimization and post-optimality analyses of optimized solutions, it stretches the scope beyond an optimization task and attempts to discover new
design/operational rules/principles relating to decision variables and objectives, so that
a deeper understanding of the problem can be obtained. The uniqueness of the extended
SBI approach presented in this talk lies on the applications of MCDM techniques, particularly the reference-point based approach, so that the decision maker can control both the
optimization task and the knowledge elicitation process towards his/her preferred regions
in the objective space. As illustrated with the experience learnt from the decision making
process in the industrial case studies, the paper will convince that SBI not only helping
production managers/engineers to explore optimal design and decision variable settings,
but also gaining better knowledge and insight about production systems engineering in
general.
Keywords: Data Mining, Decision Maker’s Preferences, Energy Efficiency, Evolutionary
Multi-Objective Optimization, Production, Sustainability.
330
MO2C (contributed)
Solving a Mutiobjective Dynamic Problem with a
Stochastic Transition Function and Stochastic
Objective Function by Interactive Procedure
Nowak, Maciej (*)
Trzaskalik, Tadeusz
Real-world decision problems are often dynamic. In such case, the decision-maker makes
a sequence of choices in order to maximize the total benefit resulting from the benefits
obtained at the subsequent stages of the process. However, the longer is the planning
horizon, the less certain is the result. Furthermore, serious decision problems involve
multiple and conflicting criteria. In the paper a multiobjective dynamic decision-making
problem is considered. We assume that both the transition function as well as objective
functions are stochastic. The decision maker goal is to find the most preferred solution.
The procedure we propose uses Bellman principle for finding the efficient set, and then
interactive approach for identifying the final solution. A numerical example and a realworld application are presented to illustrate the applicability of the proposed technique.
Keywords: Dynamic Programming, Interactive Methods, Multi-Objective Optimization,
Stochastic Programming.
331
TH2D (invited)
Solving Fuzzy Mathematical Programming Problems
by a Parametric Approach: a Historical View
Cruz Corona, Carlos (*)
Coelho Silva, Ricardo
Federal University of Sao Paulo
Verdegay Galdeano, José Luis
In 1974 Tanaka defined the concept of fuzzy mathematical programming applying the
theory of fuzzy sets to decision problems based on the concept of Bellman and Zadeh
for decision making under fuzzy conditions. Also, in those years Negoita and Sularia
shown that the problem of determination of a maximizing decision can be reduced to a
mathematical programming problem. So then, a lot of models, solutions and real-world
applications were developed. Using a dual parametric approach was one of the most interesting idea that appeared in those days for solving mathematical programming problems.
In this work, we present a historical view of this approach, and describe some of their
applications to different problems.
Keywords: Fuzzy Mathematical Programming.
332
TU5F (contributed)
Solving Linear Fractional Programming Problems
Using Multiobjective Optimization Techniques
Hasannasab , Maryam (*)
Kharazmi University of Tehran
Ehrgott, Matthias
This paper describes an approach for solving linear fractional programming problems via
reformulation as a bi-objective linear programming (BOLP) problem. We apply Benson’s
outer approximation algorithm for BOLP as presented in Ehrgott, Loehne, and Shao
(2011) in order to solve the BOFP problem and derive the optimal solution for the LFP
in the process. We contrast the performance of the primal and dual formulations of Benson’s algorithm with established algorithms for the LFP on various test problems drawn
from the literature.
Keywords: Fractional Programming, Multi-Objective Optimization, Outer Approximation.
333
WE2B (contributed)
Solving Ranking Problems with ELECTRE-III in
Case of Hierarchical Family of Criteria
Del Vasto, Luis (*)
Universitat Rovira i Virgili
Slowinski, Roman
Valls, Aida
Zielniewicz, Piotr
The current ELECTRE-III method deals with criteria defined on a common level only.
In some real-world problems, however, criteria are organized hierarchically. The leaves of
the hierarchy tree correspond to elementary criteria on which a finite set of alternatives is
directly evaluated. Then, elementary criteria are grouped in more general (intermediate)
criteria, up to a root node that represents a final decision. We propose an extension of
the ELECTRE-III method to deal with the hierarchical organization of criteria, called
ELECTRE-III-H. At a node being the parent node for a given subset of elementary criteria, the two step process typical for ELECTRE methods is applied: (1) construction
of a credibility matrix based on the marginal concordance and discordance indices, and
(2) exploitation of the credibility matrix via distillation, obtaining a partial preorder of
alternatives. This partial preorder is interpreted as a result of evaluation of the set of
alternatives by the intermediate criterion corresponding to the parent node. Then, in
order to aggregate the set of intermediate criteria with a common parent node of the
higher level, one must take into account evaluations of alternatives in form of partial
preorders. To aggregate partial orders using the concordance and non-discordance tests
of ELECTRE-III, we first adapt the marginal concordance and discordance indices to
handle all binary preference relations (indifference, incomparability and preference) from
a given partial order, and then we use these marginal indices to calculate the credibility
degree. The distillation of the credibility matrix does not change, and it results in a
partial preorder at the parent node of the higher level. The aggregation proceeds until
the root of the tree, where the final partial order is obtained. A case study on ranking
Websites from different Touristic City Brands is presented. This work is supported by
Spanish projects DAMASK (TIN2009-11005) and SHADE (TIN2012-34369).
Keywords: Hierarchical Criteria, Multi-Criteria Decision Making, Outranking Methods,
Partial Preorder.
334
WE2F (invited)
Solving Robust Bicriteria Shortest Path Problems
Raith, Andrea (*)
Kuhn, Kenneth
RAND Corporation
Schmidt, Marie 0.2cm Schöbel, Anita
Finding robust solutions of optimization problems is important in practice and well researched for single-objective problems. However, in many applications optimization problems have two or more objective functions. Here, we extend robustness concepts to a
bicriteria optimization problem with one uncertain objective. This work is motivated by
the application of routing airplanes through convective weather, where two conflicting objective functions are given by the length and the risk of the chosen route. One can identify
efficient aircraft routes as solutions of a bicriteria shortest path problem. Pilots can select
their preferred route among those efficient routes depending on how risk-averse they are.
While the length objective is known, the risk associated with any route segment depends
on weather forecasts and is therefore uncertain. We extend standard robustness concepts
to the bicriteria case with one uncertain objective and we also propose new robustness
concepts. The robustness concepts are analyzed for several types of uncertainty sets and
the relationship between different concepts is identified. We introduce methods to solve
robust bicriteria shortest path problems for our different robustness concepts and show
computational results.
Keywords: Robustness And Sensitivity Analysis, Shortest Path.
335
MO2E (contributed)
Some Epistemological Considerations of the Goal
Programming Model
Aouni, Belaid (*)
The Goal Programming (GP) is the most known model of the multi-objectives programming tools. This model is continually fed with theoretical developments and new applications with resounding success. It is a distance function model that aggregates simultaneously several conflicting and incommensurable dimensions where the deviations between
the achievement and the aspiration levels are to be minimized. Considering several dimensions enhances the legitimacy of studying the decision-making problem from different
aspects and facets. In fact, the GP model is based on a satisfying philosophy where
the decision-maker rationality is bounded and the obtained recommendation (solution)
is the one of the best compromise that fits with his/her value system and preferences.
Despite that the GP model is not a pure axiomatic model but it does respect the general
principals of mathematical modeling. The aim of this paper is to look at the GP model
epistemological dimensions and to highlight its contribution to the multi-criteria decision
aid paradigm.
Keywords: Epistemology, Goal Programming, Multi-Criteria Decision Aid, Preference
Modeling, Satisfying Philosophy.
336
TH3G (invited)
Some Remarks about Multi-Criteria Methods into
Planning for Use and Services of Natural Areas
Antón, José (*) Andina, Diego Grau, Juan
Sánchez, Maria Tarquis, Ana
Universidad Nacional de R´ıo Cuarto
Portait, Roland
´
ESSEC Grande Ecole
de Commerce
The authors have adapted with others some simple models of multi-criteria methods for planning about general uses of lands for some areas, or about special actions for them in Argentine,
or formerly about facilities for transport or soil qualification in Spain, and they tell here some
general comments. A planning field must have a technical doctrine to know its reality, evolution, actions and effects, and convenient multi-criteria modeling may aid to improve realistic
appreciations. For soils of Madrid province they added to a FAO style worse criteria qualification a weighted functional evaluation and a necessary intermediate fuzzy assessment. They had
used previously a related weighted evaluation for Spanish banks at 1995/96 transition years. For
transport, that uses internal rate of return formats incorporating bayesian elicitations for future,
authors had compromise programming experience about new R3 highway, where crisis causes
at present that tolls are deterring users, and for the high speed train line Madrid-Valencia, in
which with ELECTRE-I, and also with AHP a bit differently, long term effects were added to
the official twenty five year horizon standard transport planning evaluations, and in fact a good
real solution was build and put in service in that sense. For election of solutions for garbage in
rural areas in Salta high valleys in Argentina they added a PROMETHEE-I method with ELECTRE inspired weights for the various criteria that appear involved. Such methods with weights
elicited for long term planning, tried in various cases, matched for main uses of soils in Chaco
Salte˜
no Argentine region, but for Arroyos Menores loess area South of Rio Cuarto in Córdoba
province the erosions required to focus more on hydraulic management and soil conservation, by
incorporating them in use of soil alternatives. Towards shorter term horizon they experimented
with continuous multi-criteria methods for more detailed planning in a sub area near Rio Cuarto.
Keywords: Analytic Hierarchy Process, Compromise Programming, Multi-Criteria Decision
Making, Outranking Methods.
337
WE1B (contributed)
Standardization of Classroom Furniture in a Higher
Education Institution
Salomon, Valerio (*)
Sao Paulo State University
Alonso, Pedro
Marins, Fernando
Sao Paulo State University
As any other organization, higher education institutions have to choose resources to perform their process. Furniture used in laboratories, offices, and even in the classrooms
are an important kind of resource. When there are two or more feasible alternatives
for furniture, choosing the best furniture becomes a decision problem. One can make a
mono-criterion decision, for instance, basing only in costs. Better than that, furniture
selection can be modeled as a multi-criteria decision problem, incorporating attributes
as user’s comfort and space usage. This work presents how a Brazilian higher education
institution applied a multi-criteria decision-making method to standardize furniture to its
classrooms. The use of four criteria, being two quantitative, enhancing the learning of
multi-criteria decision-making is a major contribution of this paper.
Keywords: Analytic Hierarchy Process, Furniture, Higher Education Institution.
338
FR3D (contributed)
Stochastic Approch Versus Optimizition Over Efficient
Set
Mebrek, Fatma (*)
ENSTP
Chaabane, Djamal
USTHB
In this paper we study the problem of optimizing a linear function over an integer efficient solution set of a Multiple objective Stochastic Integer Linear Programming problem
(MOSILP). Once the problem is converted into a deterministic one by adapting the 2levels recourse approach, a new pivoting technique is applied to generate an optimal
efficient solution without having to enumerate all of them. This method combines both
techniques, the L-Shaped method and the the combined method developed in [1]. Two
didactic examples are given to illustrate different steps of our algorithm as well as mentioning the missed efficient solution when applying the algorithm in [2].
Keywords: Multi-Objective Optimization, Stochastic Linear Program.
339
TH3B (invited)
Stochastic Ordinal Regression for Multiple Criteria
Sorting Problems
Kadzinski, Milosz (*)
Pozna´
n University of Technology
Tervonen, Tommi
Erasmus University Rotterdam
We present a new approach for multiple criteria sorting problems. We consider sorting procedures applying general additive value functions compatible with the given assignment examples. For the decision alternatives, we provide four types of results: (1)
necessary and possible assignments from Robust Ordinal Regression (ROR), (2) class
acceptability indices from a suitably adapted Stochastic Multicriteria Acceptability Analysis (SMAA) model, (3) necessary and possible assignment-based preference relations,
and (4) assignment-based pair-wise outranking indices. In this way, we combine ordinal
and stochastic analysis a unified decision aiding framework. ROR methods provide recommendations such as “depending on the chosen compatible instance of the preference
model, the alternative is assigned to class medium or good or best” and “irrespective of
the compatible model instance, the alternative is assigned to class bad”. However, our experiences indicate that the possible assignment is often rather wide, whereas the necessary
assignment is often empty. In this perspective, it is useful to answer the question about
how possible it is for a particular alternative to be assigned to each class. On the other
hand, although the SMAA class acceptability indices (CAIs) can tractably be estimated
to a reasonable accuracy, they are not exact. Thus, it is desirable to analyze CAIs in
the context of the outcomes of ROR to provide information on which assignments occur
with all, some or no compatible preference models. Furthermore, the sole SMAA method
for multiple criteria sorting problems extends ELECTRE TRI. It is a pseudo-criterion
based method, which requires preference information in the form of density functions for
the class profiles, cutting level, and weights. Their elicitation may be too demanding for
a DM. To address this problem, we adapt SMAA-TRI to apply general monotone value
functions as the preference model.
Keywords: Multi-Attribute Utility Theory, Robust Ordinal Regression, Robustness And
Sensitivity Analysis, SMAA, UTADIS.
340
TU2C (contributed)
Strategic Multi-Criteria Decision Analysis
Montibeller, Gilberto (*)
London School of Economics
The choice of successful strategic options is at the core of organizational success. There
are many challenges confronted by decision makers in these contexts. Firstly, they typically involve high levels of uncertainty, which may have a huge impact on the potential
performances of the strategic options being considered. Secondly, usually there are many
powerful stakeholder involved, either playing with the internal politics of private companies, or powerful interest groups inside and outside public organization. Thirdly, there is
an increasing trend for participation in decisions, either because it increases the commitment to the solution chosen, or because it provides procedural justice and thus legitimacy
to the decision. Fourthly, the planning horizon for many of those strategic decisions is typically very long. Fifthly, any strategic decision typically involves the pursuing of multiple
and conflicting objectives, and the setting up of strategic priorities. Finally, each strategic
option is a complex package of actions and sub-policies, so evaluating their performance
is far from trivial. The answer to these challenges is, in my view, to adapt our methods
and enhance our tools, borrowing ideas from different fields that have dealt with some of
the issues involved in strategic decision making. Recent conceptual advances in Decision
Sciences provide MCDA researchers and practitioners with more powerful toolkits than
ever before. In this talk I present some of these trends and suggest opportunities for
research and practice for Strategic Multi-Criteria Decision Analysis.
Keywords: Decision Analysis, Problem Structuring, Robustness And Sensitivity Analysis, Strategic Decisions, Strategic Planning.
341
FR2B (contributed)
Structural Modeling Approach to Multiple Criteria
Problems with Interrelated Components
Michnik, Jerzy (*)
The majority of MCDM methods follows the assumption of independence between criteria. Yet, in many cases the interrelations between criteria cannot be neglected. There
are the number of methods that have been developed in the field of structural modeling
that can cope with interrelations between the components of analyzed system and enrich the modeling approaches of MCDM. This work presents the comparison of the four
such methods: Analytic Network Process (ANP), Decision Making Trial and Evaluation
Laboratory (DEMATEL), Composite Importance (CI) and Weighted Influence Non-linear
Gauge System (WINGS).
Keywords: Analytic Network Process, Composite Importance, DEMATEL, Interrelations, Multi-Criteria Decision Analysis, Structural Modeling, WINGS.
342
TH3F (contributed)
Structured Development of Automotive
Electric/Electronic Architectures Using Evolutionary
Optimization
Dietermann, Ansgar (*)
Dresden University of Technology
Bäker, Bernard
Dresden University of Technology
Automotive electric/electronic architectures describe all electric and electrical components in a vehicle as well as their interconnection and reciprocal effects. The complexity
of these systems is increasing continuously as new electric/electronic functionalities are
added to the vehicle, and existing mechanical functionalities are exchanged by electrical
ones. The electrical power train represents both of these challenges, as the combustion
engine is replaced by an electric engine and new functionalities like the high voltage level
are introduced. The rising complexity influences the development methodology of the
architectures, which is still characterized to a huge degree by the expert knowledge and
subjectivity of the specific developer. The first step in developing a new e/e-architecture
is setting up the architectural concept. In doing this, the basic and most important decisions are made, which define the quality of the finished architecture to a huge extent.
A developing method has to be found that structures the e/e-architecture development
generically, thus increasing the transparency and traceability of the made decisions, and
that leads to an optimized architecture concept to ensure the high quality of the later
e/e-architecture. A multiple criteria decision making method based on evolutionary optimization of the logical allocation and functional integration of technical modules to
e/e-architecture components is presented. The used example is the component set and
high voltage architecture of the electrified power train. A data model is set up to describe the architectures in an accessible numerical structure. A combinatorial algorithm
is presented, which is able to construct new architecture variants and which is based on
a numerical implementation of the used developing rules. With the ability to evaluate
architectures against multiple criteria, an evolutionary optimization algorithm generates
the optimized architectural concept.
Keywords: Automotive, Combinatorial Optimization, Data Structure, Decision Making Methodology, Electric Power Train, Electric/Electronic-Architecture, Evolutionary
Optimization, Multi-Criteria Evaluation.
343
MO4D (invited)
Surrogate-Based Algorithm for Computing an Upper
Bound set for the 0/1 Bi-Objective Bi-Dimensional
Knapsack Problem
Cerqueus, Audrey (*)
Gandibleux, Xavier
Przybylski, Anthony
In this work, we consider the binary knapsack problem with two objectives and two dimensions. Our purpose is to compute a tight upper bound set for the set of nondominated
points of this problem. To do this we consider the surrogate relaxation. Its principle is
to aggregate the constraints of the problem according to a multiplier u. Thus we obtain
a single dimensional problem. In the single-objective case, the solution of this relaxed
problem allows to obtain an upper bound whose quality relies on the choice of the multiplier. The problem consisting of finding the tighest possible bound is called dual surrogate
problem. With two objectives, we propose to solve the convex relaxation of the surrogate
relaxation. It gives a upper bound set for the 2O2DKP. The upper bound sets obtained
with different multipliers are not necessarily comparable. However the intersection of
the search spaces induced by those bound sets defines a tighter upper bound set. We
propose a generalization of the dual surrogate problem for the bi-objective case. We call
it the optimal convex surrogate (OCS) upper bound set. This upper bound set can be
obtained using all the possible multipliers. Thus this is the tighest possible upper bound
set based on the convex relaxation of the surrogate relaxation for the 2O2DKP. Even if
the number of possible multipliers is infinite, the number of different upper bound sets
is finite. Properties of the different multipliers u and dominance relation between upper
bound sets are studied. An exact and an approximative method to compute the OCS
upper bound set are derived. We tested numerically those two methods on a 2O2DKP
benchmark and we compare them a state-of-the-art approximative method. The results
obtained are encouraging for the use of this bound set computation in an exact solving
method for the 2O2DKP.
Keywords: Bound Sets, Multi-Dimensional Knapsack Problem, Multi-Objective Optimization, Surrogate Relaxation.
344
TU3B (invited)
Sustainability Assessment and Spatial ANP: a
Methodological Proposal for Studying the New
Megacity Region Turin-Milan
Ferretti, Valentina (*) Bottero, Marta
Mondini, Giulio
Sustainability is a multidimensional concept that includes socio-economic, ecological, technical and ethical perspectives and thus leads to issues simultaneously characterized by a
high degree of conflict, complexity and uncertainty. When speaking about sustainability in urban and territorial planning, decision-making thus requires the consideration of
trade-offs between many, often conflicting, objectives. In this context, it is generally
agreed that Multicriteria Decision Analysis (MCDA) can offer a formal methodology to
deal with partial availability of information and conflicting objectives. Given the importance of the spatial dimension in territory-related decision-making problems, there is a
growing interest in the integration of Geographic Information Systems and MCDA methods. The result of this integration, known as Spatial Multicriteria Evaluation, provides a
tool able to support both the generation of alternatives and their comparison, taking into
account the spatial distribution of each criterion, as well as the Decision Makers’ preferences. This contribution considers the problem of sustainability assessment in urban and
territorial planning projects by making use of the spatial ANP approach. In particular,
starting from the real challenge represented by the growing megacity region Turin-Milan
in Northern Italy, the study explores the contribution of the spatial ANP for structuring
the decision-making problem under analysis, paying particular attention to the elicitation
of the priorities and taking into account the opinions of different experts in the evaluation
process. Moreover, the analysis highlights opportunities and risks of the metropolitan
region in order to support the definition of future development scenarios. This research
has an innovative value, since very few experimentations of the spatial ANP exist in this
decision-making context.
Keywords: Analytic Network Process, Decision Support, Environmental Decision Making, Historical Industrial Buildings, Spatial Multi-Criteria Evaluation, Sustainability Assessment.
345
TH4C (contributed)
Sustainable National Transport Planning –
Governance with MCDA?
Jensen, Anders Vestergaard (*) Leleur, Steen
Zietsman, Joe
Texas A&M University System
Planning for a national sustainable transport system is a complex task and with a conflict
potential since it involves taking into account a wide range of criteria. In assessment of
public policies, multi-criteria decision analysis (MCDA) seems adequate, since it facilitates the use of both qualitative as well as quantitative measurement scales, which makes
it possible to address multidisciplinary problems. MCDA has seen a widespread decisionsupport function in public decision making in sectors such as energy and environment.
Over the last decade transport planning, similar to other sector planning, has undergone a
change from traditional planning to governance. This together with an increasing emphasis on sustainable development calls for new procedures, institutions and planning tools.
Unlike cost-benefit analysis and environmental impact analysis, MCDA is rarely required
by national laws or directives. Nonetheless, examples of public support of MCDA can
be found: An EU guide points out that MCDA facilitates the participation of all actors
and helps reaching a compromise or defining a coalition of views. UK has put a focus
on MCDA by publishing a general MCDA guide for official use. Italian law states that
MCDA is required as regards project selection for public works. Several examples of use
of MCDA in the public domain exist for e.g. Sweden, Portugal and France. This paper
evaluates the use of MCDA in national transport planning by its strengths and weaknesses
in assessing the impacts of public policy options up for examination within sustainable
national transport planning. The evaluation is based on a review of identified relevant
literature. The overall key issue of this paper is to highlight the application potential
of MCDA in national transport planning and its utility to policymakers engaged in such
planning for a sustainable development of the transport system.
Keywords: Governance, National Transport Planning, Public Policy, Sustainability,
Transport Policy.
346
WE1E (invited)
Sustainable Supply Chains: Contributions from
MCDM
Karpak, Birsen (*)
Globalization places demands on supply chain management beyond pure economic issues.
Cost minimization, revenue or profit maximization by itself are not sufficient; for example:
fair labor conditions, diversity, safety, and environmentally friendly product development
(greener product design) and production (cleaner process technology) are also important
and therefore sustainable supply chains are inherently multiple criteria problems. In this
research we explore multiple criteria approaches to sustainable supply chains. Different
multiple criteria methodologies used, time distribution of the articles, journals in which
these articles published will be given. Most of the articles found in literature were case
based. It is understandable since we need to understand the problem in real setting.
This can be explained by the fact that the sustainability area is a relatively new research
field and researchers need to do more case study work to understand the real issues and
problems, something that case study methodology is well-suited for. Based on this literature search we found that, still environmental dimension dominates, social aspects are
widely ignored, and social criteria need further exploration. Our overall conclusion is that
sustainable supply chains are prosperous area for MCDM community.
Keywords: Multi-Criteria Decision Making, Sustainable Supply Chains.
347
MO2B (contributed)
Synhronous Usage of Parameterized Achievement
Scalarizing Functions in Interactive Compromise
Programming
Nikulin, Yury (*)
University of Turku
Karelkina, Volha
University of Turku
This research addresses interactive multicriteria optimization approach based on parameterized achievement scalarizing function. We introduce a method of reflecting the decision maker’s preferences by means of changing the weights in the achievement scalarizing
functions. A decision making process is simulated for the three-objective median location
problem. Preliminary computational experiments illustrate the way how synchronous
usage of achievement scalarizing functions may reduce the number of iterations in an interactive process and speed up its convergence to the most preferred solution.
Keywords: Achievement Scalarizing Functions, Interactive Methods, Location, Parametrization, Synhronous Methods.
348
MO2G (invited)
Synthetic Indicators of Mutual Funds’ Environmental
Responsibility: An application of the Reference Point
Method
Pérez-Gladish, Blanca (*)
Ruiz, Francisco
Socially Responsible Investing (SRI) is broadly defined as an investment process that
integrates not only financial but also social, environmental, and ethical (SEE) considerations into investment decision making. SRI has grown rapidly around the world in the
last decades. In the last years, given the causes of the 2008 financial crisis, ethical, social, environmental and governance concerns have became even more relevant investment
decision criteria. However, while a diverse set of models have been developed to support
investment decision-making based on financial criteria, models including also socially responsible and/or environmental responsibility criteria are rather scarce. The aim of this
paper, in which we focus on the environmental dimension, is to assist individual investors
in their investment decisions providing them with a synthetic indicator of mutual funds’
environmental responsibility, which is by nature a multicriteria concept and therefore
multicriteria techniques are to be used to measure it. The proposed approach is based
on the double (reservation-aspiration) reference point method. This scheme is applied to
each of a set of US equity mutual funds’ randomly selected, in order to determine, on the
basis of a given set of indicators, a pair of synthetic indicators that measure the weak
and the strong environmental responsibility degree of each mutual fund relying on the
particular preferences of the Decision Maker.
Keywords: Environment, Equity Mutual Funds, Finance, Reference Point, Socially Responsible Investment, Synthetic indicators.
349
MO5H (poster)
Systemic Decision Making: A New Holistic Approach
in Ahp-Multiactor Decision Making
Altuzarra, Alfredo (*)
Gargallo, Pilar
Moreno-Jiménez, José Mar´ıa
Salvador, Manuel
Systemic decision making is a new approach to dealing with complex multiactor decision
making problems in which their individual preferences over a fixed set of alternatives are
incorporated in a holistic view under the “principle of tolerance”. This new approach
integrates all of these preferences even if they are encapsulated in different “individual
theoretical models”, the only requirement being that they must be expressed as some
kind of probability distribution. In this paper, assuming that the Analytic Hierarchy
Process (AHP) is the multicriteria technique employed to rank alternatives and that a
Bayesian methodology is used to calculate the priorities of the alternatives, we present a
new methodology to integrate the individual visions of the reality. To that aim we define
a tolerance distribution which allows the incorporation into the model of the preferences
of all the actors involved in the resolution process. A mathematical justification of this
distribution, a study of its statistical properties and a Monte Carlo Method to draw samples are provided. Finally, the application of the methodology to the AHP-multiplicative
model with lognormal errors and some illustrative examples are included.
Keywords: Analytic Hierarchy Process, Bayesian Inference, Cognitive Perspective, MultiActor Multi-Criteria Analysis, Principle Of Tolerance, SIR, Systemic Decision Making,
Tolerance Distribution.
350
TH2B (contributed)
The Bi-Criterion Adaptive Stochastic Knapsack
Problem
Andersen, Kim Allan (*)
Aarhus University
Nielsen, Lars Relund
Aarhus University
Ehrgott, Matthias
Pretolani, Daniele
UNIMORE
We have a known capacity of a resource, and a set of projects. Each project requires some
units of the resource which is given by a discrete probability distribution. The resource
requirements become known when a project has been selected. In that case two rewards
are received, which only depend on the project chosen. The goal is to design a set of resource adaptive strategies for choosing the projects such that the total expected value of
the two objective functions is maximized. vskip 0.3 cm We describe a two-phase method
for solving the problem, based on the hypergraph concept. Preliminary computational
results are presented.
Keywords: Adaptive Strategies, Bicriteria Model, Stochastic Knapsack.
351
TU3C (contributed)
The Decision Making Model of Service Improvements
of Smart Home System based on IOA-NRM Approach
Huang, Chung-An (*)
National Chiao Tung University
Internet applications are becoming more and more popular and the development of information and communication technologies and cloud database substantially change the
lifestyle of modern family. Chip technology industry is leading smart home as a new
blue ocean market of computer network applications. In recent years, SHSs (Smart home
system) are more and more popular due to internet penetration and the development of
private cloud system. The SHSs offer a convenient lifestyle by integrated service platforms
supporting many devices and services database. This study tries to discuss the driving
forces of the value of SHSs. The issue of SHS has become a trend and researchers began to
put emphasis on the future application in SHSs and how the SHSs change the users’ daily
life. Under the trend of smart home market prices gradually decline, multifunction computer home users gradually increase, as well as the growth number of broadband Internet
home users, SHSs (Smart Home System) experience rapid development and diffusion. The
main function of current service in SHSs is to share files as well as sharing broadband
Internet resources between family users. Future multimedia entertainment applications
are expected to be able to cause family consumers being interested in smart home system
products, which is currently the main purpose of smart home industry. The traditional
MCDM (Multiple criteria decision making) evaluation model does not consider the structural ration of the aspect/criteria of evaluation. However, the structural ration problem
still exists in the decision system. As a result, some researchers have developed a new
structural ration technique to improve upon the original MCDM model. This study develops a novel MCDM approach to solve the structural ration problem. We demonstrate
the effectiveness of the model by applying empirical location-based services cases.
Keywords: DEMATEL, Network Ration Map, Smart Home System.
352
TH4C (contributed)
The Derivation of Weights for Sustainability Criteria:
A Framework for Corporate Assessment in Sugar
Manufacturing
Sureeyatanapas, Panitas (*)
Bamford, David
University of Huddersfield
Yang, Jian-Bo
In this study, the Evidential Reasoning (ER) approach is applied in assessing corporate
sustainability performance. By employing the ER theory, a robust self-assessment model
can be developed, which is able to logically integrate both quantitative and qualitative
criteria and to capture various forms of uncertainties in subjective evaluation. As the
ER theory assumes that each criterion plays a role which is equal to its weight, a logical
procedure to justify and investigate weights is important. The objective of this paper is
to investigate the importance of each sustainability criterion by focusing on the case of
sugar manufacturing companies. The importance of each criterion, defined as its power
in discriminating different companies in terms of their ability to maintain businesses in
the long run, is analysed by means of both relative weight and its real power. The latter
is considered from the assumption that the degree of the real power of each criterion
might be higher or lower than its relative weight, particularly when an overlap among
several criteria exists. The weights are investigated by means of a questionnaire and interviews. The respondents of both methods are the managements of sugar manufacturing
companies in Thailand. The weights of criteria lying on the same branch of the hierarchy
are analysed relatively. The weight vectors deduced from the interviews are combined
based on the Weighted Geometric Mean (WGM) method. The results are cross-compared
with the weight intervals determined from the questionnaires. The possibility of the difference between the relative weight and the actual weight of each criterion is deduced
based on statistical analysis. The weight information can be used to support complex
decision-making processes such as corporate sustainability assessment. The findings are
also meaningful in the sense that the relative weights may not always precisely explain
actual contributions of the criteria toward the overall objective.
Keywords: Criteria, Multi-Criteria Decision Analysis, Multi-Criteria Decision Making,
Performance Measurement, Sustainability, Sustainability Assessment.
353
WE2C (contributed)
The Evaluation of Competitiveness of Visegrad Four
NUTS 2 Regions by the Hybrid Eigenvalue-Fuzzy
Cognitive Map Approach
Kiszova, Zuzana (*)
Mazurek, Jiˇr´ı
Nevima, Jan
The world of tomorrow is the world of the most competitive ones, and this concerns individuals as well as enterprises, regions or whole countries or unions of countries. The
aim of this article is to evaluate competitiveness of Visegrad four NUTS 2 regions by
the hybrid eigenvalue-fuzzy cognitive map approach developed by Mazurek and Kiszova
(2012), which is an MCDM alternative to the standard analytic network process by T. L.
Saaty. Regions were compared by the following criteria: gross domestic product (GDP),
gross fixed capital formation (GFCF), net disposable income (NDI), employment rate
(ER), knowledge intensive services (KIS) and patents (PAT) during programming period
of years 2000 – 2006. The most competitive regions were found as well as the least competitive ones. These findings can be useful especially for the latter regions, which can
adopt measures (collaboration, investment, benchmarking, etc.) necessary to catch up
with the more competitive regions, and also for policy makers, as regional or national
governments might stimulate the development of the least competitive regions e.g. from
European structural or cohesion funds.
Keywords: Competitiveness, Fuzzy Cognitive Map, Hybrid Eigenvalue, Multi-Criteria
Decision Making, Regions, Visegrad Four.
354
MO3F (contributed)
The Global WASF-GA: An Evolutionary Algorithm in
Multiobjective Optimization to Approximate the
whole Pareto Optimal Front
Ruiz, Ana Belén (*)
Luque, Mariano
Saborido-Infantes, Rubén
In this paper, we propose a new evolutionary algorithm for multiobjective optimization
called the Global Weighting Achievement Scalarizing Function Genetic Algorithm (Global
WASF-GA), which is based on the preference-based EMO algorithm called WASF-GA.
The main purpose of the new algorithm is to approximate the whole Pareto Optimal front,
for what an achievement scalarizing function (ASF) based on the Tchebychev distance
is used as the fitness function. This ASF depends on a reference point, which contains
desirable objective function values, and on a vector of weights. In our algorithm, an
utopian objective vector is used as the reference point, and we have considered a set of
weight vectors which are as well-distributed as possible in the weight vector space. Basically, the main idea of the Global WASF-GA is to approximate the whole Pareto optimal
front by projecting the utopian vector using this ASF and varying the vector of weights
in the whole weight vector space in order to cover the whole Pareto Optimal front. For
that purpose, at each generation, the individuals in the current population of parents and
offspring are classified into different fronts in the following way: each front is formed by
the solutions with the best values of the ASF for each one of the weight vectors in the
sample. When a front is obtained, the solutions which has been included in that front are
removed temporarily from the population and, subsequently, the next front is obtained in
the same way. This process is repeated until every individual has been included in some
front. The fact of using well-distributed weight vectors and varying them in the ASF, as
well as considering an utopian vector (which dominates the ideal vector) as the reference
point, enables us to obtain a final set of nondominated solutions that can approximate
the whole Pareto front.
Keywords: Achievement Scalarizing Functions, Evolutionary Multi-Objective Optimization, Multi-Objective Optimization, Pareto Optimal Solutions.
355
TU4B (contributed)
The Optimal Resort Development Investment Projects
with a Replacement Cost Perspective
Chang, Chiao-Chen (*)
National Dong Hwa University
Lin, T. Tyrone
National Dong Hwa University
While the past research on the topic has generally examined the resort and theme park
projects related to modeling the project decision-making and investment schedule optimization, numerous questions exist about why companies choose to invest in the resort
and theme park development projects. We address the concept of comparing a complete
cycle of replacement cost by providing a new model as it relates to follow a random Poisson
distribution under the traditional calculation method of the net present value in different
projects. The modeling results performing the model calculations show that choosing the
full region type project is the optimal resort development project. However, a sensitivity
analysis for several parameters such as the initial investment, the per unit time of the
average customer arrival rate, the per unit time of the expected variable cost for a person,
and the risk premium adjusted discounted rate is also conducted in this paper. These
findings have important implications to the leisure investment companies alike as greater
efforts are made to choose the optimal resort and theme park development projects.
Keywords: Decision Analysis, Net Present Value, Replacement Cost, Resort Development Investment, Stochastic.
356
TU2C (contributed)
The Relative Importance of the Criteria and the Ratio
of the Directional Derivatives
Sarkisyan, Rafael (*) Eskova, Olga
The construction of effective interactive algorithms and procedures for solving applied
multiple criteria problems of planning, projecting and management inevitably makes it
necessary to take into account informal information about the relative importance of the
criteria themselves. The schemes proposed by A. Geoffrion and his school back in 1970ies, based on the concept of “indifference curves” and “ideal proportions”, in principle
allow to solve a multiple criteria problem with the help of convergent man-machine algorithms, provided the criteria functions are continuous, and the set of solutions, convex
and compact.
The mechanism of the “ideal proportions” is based on the replacement of the relative
importance of criteria ωij = (∂u/fi )/(∂u/fj ) by the ratios ωij = δi /δj , i, j = 1, . . . , m,
where δi and δj are “ideal proportions” along the direction of steepest ascent of the
utility function u(f ) in the plane (fi , fj ), the values of the other criteria being fixed. The
quantities δj can be expressed through the direction e of steepest ascent of the utility
function in the set of solutions D as δj ≈ fj (xk + σe) − fj (xk ), j = 1, . . . , m, where xk is
the current point, σ is the step magnitude, such that xk + σe ∈ D, σ ∈ (0, ρ), ρ 0.
Decomposing the function δj (σ) into series and using the expression for the derivative of
differentiable directional functions dfj (xk )/de = limσ→0+ (fj (xk + σe) − fj (xk ))/σ = S jT e,
where S j is the gradient of the function fj (x), j = 1, . . . , m, for ωij one can construct the
ratio ωij = S iT e/S jT e, i, j = 1, . . . , m. For the pair of target functions (fi (x), fj (x)) they
connect the ratio of subjective estimates δi and δj with the ratio of directional derivatives
S iT e/S jT e, i, j = 1, . . . , m. These ratios have a number of effective analytical properties;
they can be used as a criterion of coordination and correction of quantities δi and δj , as
well as for constructing forecasts regarding the preferred directions of motion in the set D
and, finally, for expanding the conception of methods of possible directions of Zoutendijk
as applied to the multiple criteria problems.
Keywords: Ideal Proportions, Indifference Curve, Man-Machine Algorithm, Multi-Objective Optimization, Ratio Of Directional Derivatives, Relative Importance.
357
WE2B (contributed)
The SMAA-PROMETHEE Methods
Figueira, Jose
Greco, Salvatore
We apply the Stochastic Multiobjective Acceptability Analysis (SMAA) to the classical
PROMETHEE methods and to the Bipolar PROMETHEE methods. Bipolar PROMETHEE methods extend the classical PROMETHEE methods to the case of interacting
criteria considering the bipolar approach to Multiple Criteria Decision Aiding (MCDA)
and aggregating positive and negative preferences by means of the bipolar Choquet integral. SMAA explores the whole set of parameters compatible with some preference
information provided by the DM taking also into account imprecision on the evaluation
of alternatives on considered criteria. We compare the results given by SMAA approach
with the results supplied by Robust Ordinal Regression (ROR) that has been recently
proposed to derive robust conclusions through the use of the concepts of possible and
necessary preferences.
Keywords: Bipolar Method, Interaction Among Criteria, PROMETHEE, Robust Ordinal Regression, SMAA.
358
TH3D (contributed)
The Support of Decision Criteria Weights Elicitation
based on their Probabilistic Characteristics
Brzostowski, Jakub (*)
Silesian University of Technology
Roszkowska, Ewa
University of Bialystok
Wachowicz, Tomasz
Based on the data gathered by the Negotiation Support System - Inspire we analyze the
probabilistic patterns occurring in the collective preferences of large group of decisionmakers preparing to conduct negotiations in the stage of their pre-negotiation phase. The
behavior of negotiators in the preparation phase indicates on some regularities in the distributions of vectors of criteria weights that allows to form a probabilistic characteristic
of these crucial preferences’ elements. After performing the normality tests we discovered
that if the issues (criteria) are ordered from the most to the least important than the
pairs of consecutive issue weights are normally distributed. Such an observation allows us
to use the mean values, standard deviations, and correlation levels to describe the preferences of a population of decision-makers. The final result of our research is a decision
supporting tool facilitating the process of decision criteria weights elicitation by allowing
the decision-maker to have insight into collective preferences. Such a supportive tool may
be used in variety of decision problems in the preliminary step of analysis followed by the
use of some of the MCDM methods to conduct the next steps of preferences’ analysis. The
illustration of marginal and conditional probability distributions over the issue weights
allows to gain an intuition about global preferences and to manipulate the potential issue
weights resulting with an indication of the part of population having similar preferences.
Any manipulations with particular issue weight result in the modification of conditional
distributions corresponding to the remaining issues since there is significant correlation
between different issues related to the decision problem. The aim of the proposed system
is the indication how the adjusted preferences of the decision-maker relate to the global,
synthetic preferences of a population of decision-makers solving similar problems in the
past.
Keywords: Applied Probability, Decision Maker’s Preferences, Decision Support Systems.
359
TU2G (invited)
The Treatment of Actuator Failures in Mechatronic
Systems by Multi-objective Optimization Methods
Horenkamp, Christian (*)
University of Paderborn
Dellnitz, Michael
Ober-Blöbaum, Sina
Timmermann, Robert
Many modern mechatronic systems are desired to be optimal with respect to several
concurrent objectives such as energy and comfort. There exist many well-known multiobjective optimization methods for the numerical computation of the so-called Pareto
optimal configurations. In this talk we consider actuator failures in mechatronic systems.
Generally, in case of an actuator failure, the once chosen working point will not be Pareto
optimal anymore. Therefore, a new optimal configuration has to be calculated. Additionally, the new working point should be as close as possible to the initial point in objective
space. Classical optimization approaches would calculate the whole set of Pareto optimal
configurations for every possible failure case already during the development of the system.
This is numerically very costly in systems with a high number of actuators and therefore
often not practicable. We present a method which is invoked at the occurrence of an
actuator failure and cal-culates a new Pareto optimal configuration during runtime of the
system without explicitly calculating a whole set of Pareto optimal configurations. This
method is based on path-following techniques for parameter-dependent multi-objective
optimization problems and will be illustrated by the active suspension module of a railbounded vehicle.
Keywords: Actuator Failure, Parameter-Dependent Problem, Path-Following Methods.
360
MO3F (contributed)
Theoretically Analyzing Simple Interactive
Evolutionary Multiobjective Optimizers
Brockhoff, Dimo (*)
INRIA Lille - Nord Europe
Evolutionary Multiobjective Optimization algorithms (EMOAs) typically aim at finding
an approximation of the entire Pareto front. Interactive EMOAs, however, have been proposed in recent years to focus the search only on regions of the front in which a decision
maker (DM) is interested and which she/he is defining by revealing information about
preferred solutions successively during the search. While several interactive EMOAs are
available, it is difficult to compare the approaches and to make some recommendations
about which method to use in practice. Moreover, a thorough theoretical understanding of their runtime behavior is lacking which, if available, would assist in choosing an
algorithm over a clearly outperformed one. In this talk, I will briefly present the first
results on theoretically proving bounds on the expected number of function evaluations
and number of DM interactions until the most preferred solution of the DM is found. The
results hold under the assumption that the DM can choose the preferred solution in a pair
of incomparable points based on her/his internal utility function. While the first results
for two simple interactive EMOAs on two pseudo-boolean test functions have been published at PPSN’2012, I will discuss in this talk new results on how the theoretical bounds
change when the interactive EMOAs are allowed to learn the utility function during the
search.
Keywords: Evolutionary Multi-Objective Optimization, Interactive EMO, Learning Utility Functions, Pseudo-Boolean Search Space, Runtime Analysis.
361
TH2G (invited)
Transforming Constraints into Objectives:
Experiments with Bidimensional Knapsack Problems
Schulze, Britta (*)
Klamroth, Kathrin
Paquete, Luis
We consider constrained combinatorial optimization problems and formulate associated
multiple objective optimization problems, where one or several of the constraints are relaxed and interpreted as additional objective functions. In this way, the trade-off between
constraint satisfaction on one hand and original objective value on the other hand can be
analyzed. As a concrete example problem, we consider bidimensional knapsack problems
(i.e., one objective and two knapsack constraints) and their associated biobjective, singleconstraint knapsack problems. It is shown that a dynamic programming based solution
approach can be adapted in such a way that a representation of the nondominated set
is obtained at little extra cost as compared to the solution of the original problem. In
this context we discuss strategies for bound computation and for handling negative cost
coefficients, which occur through the transformation. Numerical results comparing the
single- and multiple objective approaches are presented.
Keywords: Bidimensional Knapsack Problem, Constraint Handling, Dynamic Programming, Multi-Criteria Combinatorial Optimization.
362
MO2G (invited)
Tri-criterion Inverse Portfolio Optimization with
Application to Socially Responsible Mutual Funds
Wimmer, Maximilian (*)
University of Regensburg
Steuer, Ralph E.
University of Georgia
Hirschberger, Markus
Munich Re
Utz, Sebastian
University of Regensburg
We present a framework for inverse optimization in a Markowitz portfolio model that is
extended to include a third criterion. This causes the traditional nondominated frontier
to become a surface. Until recently, it had not been possible to compute such surfaces.
But by using a new method that is able to generate the nondominated surfaces of tricriterion portfolio selection problems, we are able to compute via inverse optimization
the implied risk tolerances of given funds that pursue an additional objective besides risk
and return. In applying this capability to a broad sample of conventional and socially
responsible mutual funds, we find that after the screening process there appears to be no
significant difference in how assets are allocated in socially responsible and conventional
mutual funds.
Keywords: Inverse Optimization, Multi-Criteria Decision Making, Multi-Objective Optimization, Non-Dominated Surfaces, Portfolio Selection, Socially Responsible Investment.
363
MO4E (contributed)
Triple Bottomline, Hyper-radial-Visualisation-Based
’Decision-Making by Shopping’ for a Land Use
Management Problem using Evolutionary
Multi-objective Optimisation
Chikumbo, Oliver (*)
Scion Research
Deb, Kalyanmoy
Goodman, Erik
BEACON – NSF Center for the Study of Evolution in Action
A land use multi-objective optimisation problem for a 1500ha farm with 315 paddocks was formulated with 14 objectives (maximising sawlog production, pulpwood production, milksolids,
beef, sheep meat, wool, carbon sequestration, water production, income, and Earnings Before
Interest and Tax (EBIT), and minimising costs, nitrate leaching, phosphorus loss, and sedimentation). It involved 111 land use options for most of 315 paddocks and spatial constraints that
made the optimization problem combinatorial. Each point in the search space represented a
set of land-use management actions taken within a 10-year period and their related management options spanning a planning period of 50 years. Our aim was to develop a procedure
that would reduce the cardinality of considered solutions through a transparent decision making
process involving many-objective optimization and multiple heterogeneous stakeholders in order
to address the Triple Bottomline of balancing environmental, economic and social aspects. The
optimization was achieved by using a modified Reference-point-based Non-dominated Sorting
Genetic Algorithm (R-NSGA-II) augmented by simulated epigenetic operations. The decision
making process was iterative and multi-staged. The first stage involved preference construction
using virtual reality (VR)-based visual steering achieved through a Hyper Radial Visualisation
(HRV) process that took uncertainties into account. After narrowing down to a few trade-off
solutions from the first stage, decision makers in the second stage used AHP to arrive at a single
compromise solution. Highlights of the overall approach are the development of an innovative
epigenetics-based multi-objective optimiser, uncertainty incorporation in the search space data,
and a multi-stage decision-making on a multi-dimensional space through a VR-simulation-based
visual steering process controlled at its core by MCDM-based processes. The proposed approach
has widespread applicability to many other ”wicked” societal problems of interest.
Keywords: Evolutionary Algorithm, Hyper Radial Visualisation, Hyperspace Pareto Frontier,
Multiplicative Analytic Hierarchy Process, Reference Point based NSGA-II, Triple Bottomline,
Visual Steering.
364
FR2E (invited)
Two-Phase Method for Multiobjective Mixed 0-1
Linear Programming
Vincent, Thomas (*)
Gandibleux, Xavier
Przybylski, Anthony
Many concrete and important problems can be formulated by a mixed-integer linear programme. For these problems, several conflicting objectives can have to be taken into
account, leading to a multiobjective programme. Mavrotas and Diakoulaki (1998,2005)
have proposed a branch and bound method aimed at solving multiple objective binary
mixed integer programmes. In the original paper, some dominated points may remain
in the output of the algorithm and this issue was only partly addressed in the latter.
Vincent et al (2013) proposed a corrected method based on an appropriate representation of the nondominated set. In fact, continuous sets of nondominated points must be
taken into account in addition to discrete points. Those corrections were only intended
for biobjective problems, thus it remains to handle three and more objectives. In this
talk, we will first discuss the difficulties when three objective functions are considered.
Besides the expected loss of the natural order for nondominated points, some others are
typical of mixed integer programmes. For instance, a face filtered by dominance may
be not convex. We propose an adapted representation of the nondominated set for the
three-objective case with an update procedure. A branch and bound algorithm can next
be applied. Finally, we consider an adaptation of the two-phase method that was already
applied for the biobjective case. The difficulty lies here in the determination and the efficient partitioning of the search area. Indeed, if in the combinatorial case, the search area
can be defined only using corner points, here corner edges are also necessary. We propose
a projective algorithm to update the description of the search area for the mixed-integer
case using all known feasible points (and faces). This algorithm is a generalization of
(Przybylski et al. 2010). The proposed algorithms are tested on various instances and
their results are discussed and compared.
Keywords: Multi-Objective Mixed Integer Programming, Two-Phase Method.
365
TH4E (contributed)
Unsupervised Update of the Preferences on Numerical
and Multi-Valued Categorical Criteria from the
Analysis of the User Behaviour
Valls, Aida (*)
Isern, David
Marin, Lucas
Moreno, Antonio
The quality of multi-criteria decision support systems (MCDSS) depends heavily on their
knowledge about the user preferences on the values of numerical and categorical attributes.
There are many scenarios in which MCDSS are continuously interacting with the user (e.g.
filtering news from media or from social networks), and the feedback obtained from the
users (e.g. the news they actually read) can be used to learn their preferences implicitly.
In our previous works it was shown how the selections of the user could be leveraged to
effectively learn a symmetric triangular utility function, whose maximum was the most
preferred value. For categorical attributes, each possible value was given a linguistic preference score. In this work we present two improvements to the learning method. For
numerical attributes, the utility function has been generalised, including five parameters:
preferred value, right/left intervals and right/left slopes. In this way the utility function
is much more expressive and it is possible to deal with a wider set of scenarios (e.g. taking
3 as the preferred value, the preference may decrease quickly below 3, but reduce more
slowly over 3). The MCDSS analyses carefully the user continuous selections to learn
these parameters for each numerical attribute. The second improvement allows alternatives to take multiple values in the categorical attributes. The presence of multiple values
makes it more difficult to analyse the user choices and to infer why the user has chosen (or discarded) a certain alternative. The profile learning method can now deal with
this kind of scenarios, being able to assign efficiently a qualitative preference score to
each possible value. This preference learning method has been tested for recommending
restaurants in Barcelona, obtaining encouraging results. This work has been supported by
a pre-doctoral grant from University Rovira i Virgili and the Spanish projects DAMASK
(TIN2009-11005) and SHADE (TIN2012-34369).
Keywords: Decision Support Systems, Multi-Criteria Decision Making, Preference Learning.
366
MO5H (poster)
Use of Fuzzy ANP-VIKOR Technique in
Multi-Criteria Decision-Making of Satisfaction
Evaluation for Business-to-Customer (B2C) Services
Improvement
Chern , Yuching (*)
National Chiao Tung University
Tzeng, Gwo-Hshiung
Kainan University
Customer satisfaction in e-commerce (e-satisfaction) is a key determinant that promotes
the repurchase actions at Website stores and creates marketing values for business-toconsumer (B2C) service firms. However, while the measured values of human perceptions
towards various criteria are often vague data, most conventional measurement methods
can not precisely handle imprecise numerical quantities. Therefore, this paper aims to
present a fuzzy multiple-criteria decision-making model with fuzzy ANP-VIKOR approach
to manage the assessment problem of B2C service e-satisfaction measures. First, the analytic network process (ANP) is applied for determing weights of essential factors that
enhance B2C service e-satisfaction. Secondly, the logic of triangular fuzzy numbers are
used in developing the fuzzy VIKOR algorithm for prioritization among the alternative
B2C services providers and for identifying the satisfaction gaps between alternatives and
the aspired-level, specifically intended to reduce gaps for B2C service improvement. An
empirical case illustrates the applicability and effectiveness of the proposed fuzzy ANPVIKOR method, and the result by the TOPSIS method is compared. The multi-criteria
consumers’ e-satisfaction measures derived from this study can further support decisionmakers in service contents selection, and indicate solutions to enable customer satisfaction
with B2C services improvement.
Keywords: Analytic Network Process, E-Satisfaction, Fuzzy Logic, Multi-Criteria Decision Making, VIKOR.
367
FR2D (contributed)
Use of Multiattribute Utility Function for a
Cost-Utility Analysis Kangaroo Mother versus
“Traditional” Care for Premature Infants in Bogota,
Colombia
Castillo, Mario (*) Bernal, Astrid R´ıos, John-Jairo
Universidad de los Andes
Charpak, Nathalie
Kangaroo Foundation
Córdoba, Mar´ıa-Adelaida Ruiz, Juan-Gabriel
Universidad Javeriana
Trujillo, Tammy
During last years, the high impact of prematurity on mortality and morbidity of newborns has
been identified. Currently, the standard care given to premature newborns is to keep the newborn hospitalized in a neonatal unit. Another alternative is the Kangaroo Mother Program
(KMP) which aims to obtain a significant reduction in hospitalization costs of newborns and
nosocomial infections, among others. A cost-utility analysis (CUA) of alternatives was performed, in which utility of the treatments was estimated with a multicriteria model. As a part
of CUA, a methodology was designed to evaluate utility of medical treatments using MAUT approach, which was applied specifically to treatment of premature newborns in Bogota, Colombia.
The methodology starts constructing a multiattribute utility function. Next, it was defined the
weight of each variable, and utility of each states. Finally, the average utility of newborns in
each treatment was estimated. Currently, cost estimation is in process and is not included in
this presentation. Measuring utility was established with the participation of a group of medical
experts in pediatrics. The models and the analysis were constructed based on the study results
Charpack, Ruiz, Calume, & Charpack, 1997, that built a database of 593 premature newborns.
A second model was built, in which mutually exclusive health states were defined and scored,
and it was used to validate results. The main result of the implementation of the methodology
was the construction of a model for evaluating the utility of the newborns. The multicriteria
model results showed statistically significant difference between treatments in favor of KMP.
Similar results were obtained by the second utility model. The multiattribute utility function
constructed enabled to evaluate utility of newborns health state, and compare both treatments
analyzed. It allowed capturing judgments of experts in a rigorous way.
Keywords: Cost Utility Analysis, Cost-Effectiveness Analysis, Multi-Attribute Utility Theory, Premature Newborns Care.
368
TU3F (invited)
Using Choquet Integral as Preference Model in
Interactive Evolutionary Multiobjective Optimization
Branke, Juergen (*)
University of Warwick
Corrente, Salvatore Greco, Salvatore
Slowinski, Roman
We propose an interactive multi-objective evolutionary algorithm (MOEA) that attempts
to discover the most preferred part of the Pareto-optimal set. Our approach is inspired
by the approach of Greenwood, Hu and D’Ambrosio (1996) which ranks a solution better than another if it is preferred by all the utility functions compatible with pair-wise
preference information supplied by the user. Our approach differs from the approach
by Greenwod et al. because instead of modeling user’s preferences by a linear utility
function we consider a more flexible utility function - a Choquet integral. We show that
our approach works well even in situations where preferences can not be expressed by
a linear utility function (and the approach by Greenwood et al. fails to find the most
preferred solution). In case the linear utility function can express the user’s preferences,
our approach is able to get the same result as the Greenwood et al. approach, although
it converges a bit slower.
Keywords: Choquet Integral, Evolutionary Multi-Objective Optimization, Preference
Learning.
369
TU4E (contributed)
Using MACBETH with the Choquet Integral to
Model Interdependencies between Indicators in the
Context of Risk Management
Lopes, Diana F. (*)
Bana e Costa, Carlos
University of Lisbon
Risk management typically demands for comparing the consequences of different sources
of risk on multiple dimensions. Multicriteria value models can assist in evaluating those
consequences. Often several impact indicators should be considered in some dimensions
and, therefore, modelling should identify and account for possible judgemental interactions
between indicators. The Choquet Integral (CI) has been used for this purpose, namely for
industrial performance aggregation, and many applications of the 2-additive CI operator
have been reported in the literature, for it is argued that 2-additivity is probably the
best compromise between complexity and richness of the model. In this framework, the
MACBETH technique is used to determine the parameters of the Choquet model, from
n+1 sets of pairwise qualitative judgements of difference in attractiveness, one for each
one of the n indicators separately and another one for pairs of interdependent indicators.
However, all the MACBETH judgements assessed are judgements of global attractiveness, and therefore we propose that it is more adequate to build one single MACBETH
global matrix and determine all of the Choquet parameters directly from it. The proposed
procedure is applied to the evaluation of the consequences of sources of risk in the risk
management context at the ALSTOM Company.
Keywords: Choquet Integral, Interactions, MACBETH, Risk Management.
370
TH4E (contributed)
Using Methods of Multi-Criteria Decision Making to
Evaluate the Sustainability of Bioenergy Concepts by
Integrating Adequate Reference Points
Lerche, Nils (*)
University of Goettingen
Geldermann, Jutta
Biomass usage for energy conversion is often associated with sustainability, as it is considered more favorable with regard to climate change than fossil fuels. Nevertheless, there are
also significant concerns, particularly about the need for subsidies, the increase of monocultures or the competition for soil with food crops. Thus, it becomes apparent that the
sustainability evaluation of biomass usage concepts is of multi-criteria nature and should
not only imply economic, but also environmental, social and technical aspects. Hence,
methods of Multi-Criteria Decision Analysis (MCDA) appear to be suitable. However,
in order to evaluate different concepts with respect to sustainability, it is not sufficient
just to compare several alternatives on the basis of their performance concerning different criteria. To identify suitable sustainable concepts, the integration of an adequate
reference point is needed. Through this reference point it should be facilitated that a
concept actually fulfills the requirements for sustainability instead of merely being better than other alternatives. Hence, the identification of a suitable reference point, which
could be applied as indicator for sustainability within methods of MCDA, is crucial. Another aspect of increasing interest is the actual decision behavior of decision makers and
the consideration of behavioral effects in MCDA-methods. To combine the need for a
suitable reference points in combination with behavioral effects, the integration of elements of prospect theory appears to be a meaningful enhancement. Therefore, potential
extensions and adjustments of existing concepts for combining the multi-criteria method
PROMETHEE with prospect theory will be discussed to address the need for an adequate
reference point and the integration of behavioral effects, enabling an improved assessment
with respect to sustainability. In addition, a bioenergy-related example is presented to
illustrate the enhancements.
Keywords: Bioenergy, PROMETHEE, Prospect Theory, Reference Point, Sustainability.
371
FR3E (contributed)
Using Structured Decision Making to Facilitate
Environmental Risk Management
Liu, Shuang (*)
CSIRO Ecosystem Sciences
Environmental managers and policy-makers face two major challenges when making decisions involving environmental risks. First, management decisions frequently involve
trade-offs between complex and often competing environmental, social and economic consequences with potential positive or negative outcomes for different social groups. Second, understanding of these consequences is often marked by profound uncertainties.
When combined, these challenges too often become excuses for maintaining the status
quo instead of considering alternatives that might result in net social welfare gains. One
emerging decision-aid tool that combats these two challenges by taking into account social
values and uncertainty is Structured Decision Making (SDM). Compared to MCA without
a public involvement component, SDM provides an opportunity for diverse stakeholder
views to be explicitly incorporated within the decision-making process. In addition, the
SDM can also function as a communication platform, whereby scientists, stakeholders and
decision-makers can interact and discuss the uncertainties associated with environmental
decisions. Thus, SDM injects scientific rigor and transparency into the decision-making
process by providing an analytical structure for social complexity and an interacting forum for risk communication. In this presentation, we situate our methodology within
the environmental management and MCA literature, discussing the advantages and limitations of SDM as a framework to support environmental risk management. We also
discuss the potential for integrating SDM with other decision-aid tools such as decision
trees, cost-benefit-analysis, and interactive gaming models. A decision support system
incorporating SDM and these other tools greatly enhances the capacity of SDM in evaluating creative alternatives and addressing explicit tradeoffs associated with environmental
risk management.
Keywords: Biosecurity Management, Decision Support Systems, Environmental Decision Making, Group Decisions Making, Risk Communication, Risk Management, Scientific
Uncertainty, Water Resources Management.
372
TH2C (contributed)
Value Efficiency: A General Case
Soleimani-Damaneh, Majid (*)
University of Tehran & IPM
Value Efficiency Analysis (VEA) is an approach for measuring the relative efficiency in
DEA, Multiobjective Optimization and MCDM, incorporating the decision maker’s preferences explicitly. This technique allows us to use the value judgments in performance
analysis and decision making. This paper provides new connections between different
definitions of value efficiency. The importance of the (Lower semi/Upper semi) continuity of the value function is highlighted. Some problematic situations are illustrated by
discontinuous examples. Also, a connection between cone-preferences and value functions
is addressed. A main theorem is established providing a sensitivity-analysis-based result.
It is shown that the value inefficiency with respect to a wide class of value functions can
be recognized by obtaining the optimal value of an optimization problem. This class of
functions contains some usual forms, including Locally Lipschitz concave functions and
pseudo-concave ones. Also, we incorporate more general value functions in VEA, using
the global optimality notion. Another part of this talk is devoted to nonsmooth value
functions with respect to Clarke generalized gradients. Some properties of these functions are investigated. Mean-value theorem, closedness and boundedness properties of
the Clarke generalized gradients set play a vital role in this investigation. Also, weightrestricted models are studied and some of their properties are addressed.
Keywords: Data Envelopment Analysis, Multi-Objective Optimization, Value Function.
373
MO4C (invited)
Variational-Like Inequality Problems as an Important
Tool to Solve Vectorial Optimization Problems
Ruiz-Garzón, Gabriel (*)
Universidad de Cádiz
Batista dos Santos, Lucelina
Universidade Federal do Parana
Rufián-Lizana, Antonio
The purpose of this work is to investigate the relationships between solutions of Stampacchia and Minty vector variational-like inequalities and Pareto and weak Pareto solutions of
vector optimization problems in Banach spaces under pseudo invexity and pseudo monotonicity hypotheses. These results enable us to relate Pareto and weak Pareto points and
solutions of the weak and strong Minty, Stampacchia and perturbed vector variationallike inequalities problems. Our results extend and improve the corresponding results of
others in finite dimensional spaces.
Keywords: Convex Programming, Multi-Objective Optimization.
374
TH4E (contributed)
Web Multicriteria Spatial Decision Support System:
Integration of a Web-based ELECTRE TRI in ArcGIS
Silva, Sandra (*)
Instituto Politécnico de Viana do Castelo
Al¸cada-Almeida, Lu´ıs Dias, Luis
University Coimbra
Solving spatial multicriteria decision problems can benefit from both Geographic Information Systems (GIS) and Multicriteria Decision Aid (MCDA). A useful support is provided
by a specific family of Spatial Decision Support Systems (SDSS) named Multicriteria
Spatial Decision Support Systems (MC-SDSS), which are based on GIS and MCDA integration. MC-SDSS tools offer unique capabilities for automating, managing and analyzing
spatial decision problems with large sets of feasible alternatives and multiple conflicting
and incommensurate evaluation criteria. The conceptual idea in this study is based on the
use of GIS capabilities to develop an adequate platform in order to use multicriteria methods available in a Web-based MCDA Server. A Web MC-SDSS methodological framework
is proposed for a fully integrated system of GIS and ELECTRE TRI in ArcGIS software
of ESRI that interacts with a Web Algorithms Server for MCDA techniques application.
The developed Web MC-SDSS is tested on a case study analyzing environmental and
socioeconomic sustainability of dairy farms in Entre-Douro-e-Minho Region.
Keywords: Decision Support Systems, Multi-Criteria Decision Analysis, Outranking
Methods, Spatial Multi-Criteria Evaluation, Sustainability Assessment.
375
TH4D (contributed)
Web-Based Decision Support System for Solving
Multiple Objective Linear Integer Problems (MOLIPs)
Kirilov, Leoneed (*) Genova, Krasimira
Gouliashki, Vassil Staykov, Boris
Bulgarian Academy of Sciences
The DSS WebOptim has the following main features: Web accessible, User-friendly,
Solver-universality, Heterogeneity. The main optimization solver is the generalized scalarizing method GENS-IM. It incorporates twelve multiple objective methods. Also an evolutionary multiple objective method for generating approximate Pareto optimal solutions
is available. Solving the MOLIPs by means of DSS WebOptim is very easy: 1) Each
user creates personal account in the system by using simple method for web registration
with username and password; 2) When the user is logged in, he/she is able to define
or edit single or multi-objective optimization problems which automatically are associated with his/her personal profile; 3) To initialize solving process, an appropriate solver
must be chosen. Then the problem is parsed by a specific syntax parser. After that all
data is send to the corresponding solver by using inter-modular communication protocol
using web service standards; 4) When the solver finds a solution or error event occurs,
all data is send back to the main system, again using web service based inter-modular
communication protocol. The user can preview the solution, enter new preferences or
edit the problem and send it to the same or another solver for new solution. This process
is continuing until the decision maker is satisfied with the current computed solution.
Each solution step is kept in the system so that there is a full solution history for the
problem, allowing user to go back or forth through the steps using them for start point
for new solution. In conclusion, the DSS WebOptim is a very convenient tool for solving
MOLIPs. The DSS WebOptim realizes the contemporary tendencies and conceptions for
solving decision making problems. Acknowledgment: This research is supported in part
by Bulgarian NSF, Grant No DTK02/71 and IICT-BAS research project “Applications
of Operations Research for Decision Making in Engineering Systems”.
Keywords: Decision Support Systems, Integer Programming, Multi-Objective Optimization, Web Architecture.
376
TU2B (contributed)
What Matters to Stakeholders and Citizens in the
Evaluation of Local Policies? Using MCDA to
Understand Different Perspectives in Town Renewal
da Silva Vieira, Ricardo (*)
Antunes, Paula
Universidade Nova de Lisboa
Small towns provide many services to their citizens. Yet, during the last decade, there
seems to be a decline on the services these towns provide. These services include hospitals,
schools, transport and retail. Many policy-decisions on town renewal target behavioural
changes of their citizens. In society, there are multiple legitimate values. Decisions have
always a value frame behind. So, will the values used in decision-making be effective in
changing people’s behaviour? If a different value set was to be considered, what alternative responses might be developed? The aim of this paper was to understand how well
do the objectives/values used for the appraisal of town renewal options by policy makers represent citizens’ concerns, using Multicriteria Mapping (MCM) as a tool to elicit
stakeholders values. A case study is used: Thornbury, a small town in the Southwest of
England. Thornbury was experiencing a decline in retail in the town centre on the public
services. Two studies were conducted: one looking into retail decline and the other to
public transport decline. Individual interviews were conducted with Thornbury residents
using MCM, where participants defined their own criteria (at least three) and weights.
19 participants were used for the first study and 12 participants for latter study. Some
participants could only think of one or two criteria for the appraisal. Even though, results
showed wide range of criteria, including qualitative criteria (e.g, being a novelty in the
town, creating a purpose to come into town and Community cohesion). The range of
criteria generated shows that the technical side of the problems (e.g, infrastructure or
management related issues) are only a part of the issue. New criteria (e.g, Community
cohesion and identity and Equity for consumers), different from criteria used by local
authorities (e.g, costs of implementation, effectiveness in dealing with the issue and accessibility for retail regeneration) were deemed relevant.
Keywords: Criteria, Multi-Criteria Mapping, Participatory Multi-Criteria Evaluation,
Public Transport, Retailing, Town Regeneration.
377
List of Keywords
3-Objective Linear Programming Solver,
98
3PL, 59
Automated Mechanism Design, 130
Automotive, 343
Auxiliary Services Of Power Plants, 291
A* Search, 174, 274
Academic Performance Assessment, 121
Achievement Scalarizing Functions, 56, 166,
209, 316, 348, 355
Actuator Failure, 360
Adaptive Relaxation Of Constraints, 213
Adaptive Strategies, 99, 351
Additive Value Function, 278
Adjacency, 115
Adversarial Risk Analysis, 101
Aggregation Of Individual Priorities, 46
Aggregation Schemes, 108, 149, 245
Agricultural Public Goods, 120, 311
Agriculture, 311
Airport Security, 101
Allocation, 66, 306
Alternatives, 198
Amenities, 71
Analytic Hierarchy Process, 46, 57, 86,
96, 112, 127, 128, 141, 156, 172,
230, 238, 263, 266, 300, 306, 337,
338, 350
Analytic Network Process, 59, 65, 93, 94,
120, 140, 238, 303, 342, 345, 367
Animal Welfare, 146
Anytime Computing, 308
Applications, 100
Applications of MCDM, 52, 67, 70, 78,
90, 122, 145, 151, 160, 163, 194,
201, 242, 243, 251, 253, 254
Applied Probability, 359
Approximation, 124, 286
Approximation Algorithm, 285
Approximation of The Ideal Point, 85
Artificial Intelligence, 108, 170, 254, 274,
328
Aspiration and Reservation Values, 276
Assembly Line, 144
Assignment, 226, 238, 264
Auctions/Bidding, 155, 236
Augmented Goal Programming, 129
Bargaining, 136
Bayesian Analysis, 47, 208
Bayesian Inference, 182, 350
Behavioral, 51
Belief Decision Trees, 218
Belief Distribution, 182
Belief Rule-Based methodology, 108, 250
Bi-Objective Location Problem, 281
Bi-Objective Multi-Commodity Flow Problem, 48
Bi-Objective Ring Star Problem, 83
Bi-Objective Routing, 113
Bi-Objective Shortest Path Problems, 274
Bi-Objective Simplex Method, 48
Bi-Objective Traffic Assignment, 133
Bias, 134
Bicriteria Model, 52, 351
Bidimensional Knapsack Problem, 362
Bidirectional Heuristic Search, 135
Bioenergy, 119, 371
Biosecurity Management, 372
Bipolar Method, 358
Block Coordinate Descent, 164
Bound Sets, 344
Branch and Bound, 49, 286
378
Capability Based Planning, 57
Capital Budgeting, 156
Case Example, 185, 293
Case Study, 78, 94, 145, 150, 304
Category Size, 246
CD-MAMCA, 91
Characterization Of Statutory Zones, 71
Chemmical Engineering, 251
Choquet Integral, 92, 146, 199, 369, 370
City Distribution, 91
Classification, 79, 97, 138, 187, 218, 245,
279
Clinical Performance, 181
Coalition Formation, 257
Cognitive Map, 260
Cognitive Perspective, 350
Collaborative Planning, 143, 193
Combinatorial Optimization, 95, 113, 124,
190, 199, 206, 286, 316, 343
Comparative Analysis, 53, 142
Competitiveness, 61, 65, 96, 354
Composite Importance, 342
Compromise Programming, 55, 316, 337
Computational Cost, 81, 102, 253
Computationally Expensive Problems, 269
Computing Science, 172
Computing With Words, 194
Conflicting Criteria, 142
Conjoint Analysis, 186
Consistency, 300, 311
Consistency Consensus Matrix, 300
Constraint Handling, 213, 362
Constraint Programming, 287
Constructability, 71
Construction And Demolition Waste, 53
Control, 122, 170, 251, 308
Convex Programming, 374
Convex Sequences Of Weights, 313
Copula-Based Model, 183
Corporate Social Performance, 73
Correlation, 249
Corridor Location, 214
Cost Avoidance, 232
Cost Benefit Analysis, 306
Cost Utility Analysis, 101, 368
Cost-Effectiveness Analysis, 368
Costs, 325
Cournot Competition, 296
Covering Location Problem, 320
Creating Alternatives, 151
Criteria, 172, 243, 353, 377
Criterion Importance, 51, 146
Critical Paths, 79
Cross Entropy, 152
Culture, 229
Decision Analysis, 131, 134, 141, 148, 150,
159, 223, 226, 237, 302, 341, 356
Decision Maker’s Preferences, 64, 129, 149,
212, 245, 288, 304, 311, 330, 359
Decision Making, 92, 107, 122, 128, 129,
186, 240, 243, 253, 272, 279, 282
Decision Making Methodology, 343
Decision Space Diversity, 326
Decision Support, 71, 134, 155, 161, 176,
208, 302, 345
Decision Support Systems, 66, 117, 141,
172, 225, 226, 242, 273, 275, 359,
366, 372, 375, 376
Decomposition, 164
Decomposition Method, 48
DEMATEL, 65, 140, 342, 352
DEMATEL-Based ANP, 177
Dempster-Shafer Theory, 182
Differential Evolution, 203, 209
Distance-Based Methods, 157
Distributed Decision Making, 193
Distribution Centers Location Problem,
262
Dominance Measuring Methods, 82
Dominance-Based Rough Set Approach ,
225, 277
Duality, 270
Durability, 268
Dynamic Approach, 167
Dynamic Network, 261
Dynamic Optimization, 251
Dynamic Programming, 331, 362
Dynamic Systems, 122, 251
Dynamic VRPTW, 256
E-Commerce, 130, 140
E-Satisfaction, 367
Earliest Arrival Flow, 261
Early Admission, 121
Econometrics, 289
Economics, 130
Ecosystems, 304
Education, 117, 289
Effectiveness, 281
Efficiency, 84, 125, 126, 281
Efficient Frontier, 307
ELECTRE Methods, 77, 222, 228
ELECTRE TRI, 71
Electric Power Train, 343
Daily Used Materials, 270
Data Envelopment Analysis, 103, 181, 283,
305, 310, 313, 314, 373
Data Mining, 66, 97, 323, 330
Data Routing, 295
Data Structure, 343
De Novo Programming, 167
Decision Aiding, 278
379
Electric/Electronic-Architecture, 343
Electricity Production Technologies, 263,
265
Elementry Education, 284
Elicitation, 149, 173, 219
Embedded Systems, 271
Empirical Study, 198
Energy Efficiency, 221, 330
Energy System, 127, 185, 240, 241
Entropy, 178
Environment, 75, 145, 162, 349
Environmental Decision Making, 70, 94,
131, 149, 208, 215, 216, 299, 323,
345, 372
Epistemology, 336
Epsilon-Constraint Method, 119
Equity Mutual Funds, 55, 126, 299, 349
Erosion Control, 210
EU, 229
Eucalyptus Plantations, 210
Evacuation, 261
Even Swaps, 134
Evidential Reasoning, 121, 182
Evolutionary Algorithm, 83, 203, 364
Evolutionary Computations, 66, 109, 294,
315, 326
Evolutionary Multi-Objective Optimization, 81, 99, 152, 168, 169, 180,
192, 196, 207, 266, 294, 315, 323,
330, 355, 361, 369
Evolutionary Optimization, 166, 343
Exergy, 239, 240
Expensive Evaluations, 130
Expert systems, 217
Exponential Family, 47
Fractional Programming, 69, 242, 333
Furniture, 338
Fuzzy Cognitive Map, 354
Fuzzy DEA, 126
Fuzzy DEMATEL, 110
Fuzzy Integral, 140
Fuzzy Logic, 72, 112, 122, 153, 245, 367
Fuzzy Mathematical Programming, 58, 60,
310, 332
Fuzzy MCDM, 177
Fuzzy Measures , 280
Fuzzy Numbers, 61, 194
Fuzzy Reasoning, 108
Fuzzy Set Extension Of DRSA, 61
Fuzzy Sets, 64, 118, 127, 138, 143, 193,
195, 197
Fuzzy VIKOR, 197
Gaussian Process Modeling, 203
Generating Alternatives, 109
Genetic Algorithms, 62, 74, 109, 153, 170,
238, 257, 267
GIS, 225, 322
GIS-MCDA Integration, 225
Global Optimization, 292
Goal Programming, 64, 90, 118, 143, 157,
195, 200–202, 210, 328, 336
Governance, 346
Graph Theory, 325
GRASP Algorithm, 76
Greenhousegas Emissions, 119
Grey Numbers, 205
Group Decision Making, 46, 72, 220
Group Decisions Making, 64, 110, 118,
127, 273, 300, 304, 372
guided search, 99, 168
Facility Location Problem, 327
Factory Layout, 62
FAHP, 104, 221, 319
Favelas, 221
Feasibility Approach, 213
Feature Selection, 187
Federal Funds Distribution, 165
Finance, 176, 290, 349
Financial Performance, 73
Fleet Availability, 233
Flight And Maintenance Planning, 233
Food Supply Chains, 239
Forest Management, 69, 206, 273
Hölder Metric, 224
Harvest Scheduling, 69, 210
Healthcare, 181, 227
Heuristic Search, 174, 274
Hierarchical Criteria, 334
Hierarchical Optimization, 111
Higher Education Institution, 338
Historical Industrial Buildings, 345
Hospital Site Selection, 112
Human Development Index, 276
Human Resources, 123
Humanitarian Logistics, 78, 138, 327
380
Hybrid Approaches, 65, 66, 81, 259, 287
Hybrid Eigenvalue, 354
Hybrid Framework, 209
Hydroelectric Plant, 319
Hyper Radial Visualisation, 364
Hyperspace Pareto Frontier, 364
Learning Utility Functions, 361
Life Cycle Analysis, 240
Life Testing, 47
Line Balancing Control, 248
Linear Approximation, 307
Linear Model, 183
Linear Programming, 89, 92, 98, 123, 191,
222, 227, 288, 306, 324
Linear Scalarization, 137
Linear Transformation, 283
Linguistic Variables, 194
Location, 87, 180, 223, 348
Logistics, 116, 202
Loss Aversion, 134
LR-Type Fuzzy Variables, 196
Ideal Proportions, 357
Identifying Objectives, 151, 304
Immigration, 153
Immune Algorithm, 154
Impacts Of Statutory Planning, 71
Importance Weights, 110
Imprecise Information, 82, 92
Improving Method, 188
Incomplete Information, 220
Inconsistency, 321
Indifference Curve, 357
Indifference Thresholds, 129
Innovation Strategy, 177
Integer Linear Programming, 84
Integer Programming, 106, 114, 189, 294,
376
Intellectual Capital, 156
Intelligent Decision System, 121
Intensity Modulated Radiation Therapy,
244
Interaction Among Criteria, 219, 280, 358
Interactions, 370
Interactive EMO, 209, 361
Interactive Methods, 102, 106, 113–115,
117, 155, 279, 282, 307, 331, 348
Interactive Multi-Objective Optimization,
277
Interrelations, 342
Interval-Valued Functions, 205
Invasive Alien Species, 225
Inventory Routing Problem, 116
Inverse Optimization, 363
Investment Analysis, 176
Irrigated Olive Grove, 120
MACBETH, 260, 370
Machinery Industry, 96
Macroscopic Model, 261
Man-Machine Algorithm, 357
Management Accounting And Auditing,
201
Map Comparison, 173
Marine Engineering Systems, 122
Marketing, 234
Markov chains, 265
Matching, 88
Mathematical programming, 54, 97, 125,
187, 265
Matrix Representation, 179
Matroids, 85
Measurement, 107, 148, 150
Measures Of Sensitivity And Efficiency,
329
Mechanical Engineering, 192
Meta-Goal, 118, 143, 195
Metaheuristics, 100, 111, 222, 268
Microsoft Excel, 230
Minimal Size, 285
Mixed Integer Multi-Objective Programming, 233
Mixed Integer Programming, 49, 106, 223,
227, 232, 242, 293, 294
Model Testing And Validation, 250
Model-Based Design Of Experiments, 249
Monte Carlo Simulation, 82, 153
Morphological Analysis, 194
Multi-Actor Multi-Criteria Analysis, 91,
350
K-Min Objectives, 259
KLD Rating, 73
Knapsack, 206
Lagrangian Duality, 164
Land Use Planning, 206, 267
Large Scale Optimization, 52
381
Multi-Agent Systems, 102, 130
Multi-Attribute, 236
Multi-Attribute Utility Theory, 67, 131,
146, 149, 161, 186, 208, 237, 340,
368
Multi-Attribute Value Theory, 51, 72, 234,
288
Multi-Choice Goal Programming, 129
Multi-Constraint Levels, 179
Multi-Criteria Combinatorial Optimization,
190, 362
Multi-Criteria Decision Aid, 77, 165, 178,
219, 280, 321, 322, 327, 336
Multi-Criteria Decision Analysis, 57, 72,
84, 86, 98, 128, 165, 182, 204, 205,
216, 217, 226, 237, 240, 245, 255,
275, 276, 301, 315, 342, 353, 375
Multi-Criteria Decision Making, 53, 55,
58, 59, 65, 68, 82, 103, 106, 110,
127, 131, 142, 145, 155, 160, 162,
181, 192, 209, 230, 231, 235, 242,
258, 271, 273, 284, 298, 314, 334,
337, 347, 353, 354, 363, 366, 367
Multi-Criteria Decision Model, 79, 285
Multi-Criteria Evaluation, 65, 343
Multi-Criteria Evaluation Model, 260
Multi-Criteria Hierarchy Process, 272
Multi-Criteria Investment Problem, 224
Multi-Criteria Logic, 329
Multi-Criteria Mapping, 377
Multi-Criteria Model Predictive Control,
308
Multi-Criteria Ranking, 183
Multi-Criteria Sorting, 123, 222, 246
Multi-Dimensional, 107
Multi-Dimensional Knapsack Problem, 259,
344
Multi-Methodology, 165
Multi-Objective Optimization, 357
Multi-Objective Shortest Path Problem,
135
Multi-Objective Assignment, 152
Multi-Objective Combinatorial Optimization, 152
Multi-Objective Differential Evolution, 213
Multi-Objective Genetic Algorithms, 184
Multi-Objective Knapsack, 152
Multi-Objective Linear Programming, 179
382
Multi-Objective Mixed Integer Programming, 365
Multi-Objective Optimization, 50, 54, 56,
60, 62, 69, 74, 78, 84, 88, 89, 98,
100, 105, 106, 109, 111, 114, 116,
117, 124, 130, 132, 135, 147, 153,
154, 158, 164, 166, 167, 169, 175,
179, 189–192, 196, 199, 203, 207,
211, 214, 231, 244, 247–249, 251,
252, 255–257, 259, 261, 264, 266,
268–270, 273, 279, 282, 286, 287,
289–292, 294, 307, 320, 323, 324,
326, 328, 331, 333, 339, 344, 355,
363, 373, 374, 376
Multi-Objective Shortest Path Problem,
174, 214
Multi-Parameter Functions, 129
Multi-Project Scheduling, 235
Multi-Purpose Water Resource, 104
Multi-Scenario, 136
Multi-Stakeholder Decision Making, 304
Multi-Variate Decision Analysis, 186
Multimoora, 290
Multiplicative Analytic Hierarchy Process,
364
Multiplicative Form, 161, 290
Mutation, 209
Nadir Point, 147, 169, 189
National Transport Planning, 346
Natural Gas Pipeline, 275
NAUTILUS Method, 209
Nearly Subconvexlikeness, 137
Negotiation, 47
Net Present Value, 356
Network Ration Map, 352
Network Resilience, 326
Neural Networks, 108
New Product Development, 177, 250
NIMBUS, 102, 279
Non-Dominated Frontier, 184, 233
Non-Dominated Set, 211
Non-Dominated Surfaces, 363
Non-Linear Programming, 119
Non-Linear Multi-Objective Optimization,
56, 188, 281, 287, 307
Non-Linear Scalarization, 80
Non-Regular Problems, 171, 207
NSGA-II, 209
Objectives, 198
Obsolescence, 128
Offshore Wind Farm, 202
Open And Distance Learning, 238
Operator Allocation, 248
Optimal Control, 251, 308
Optimality Conditions, 171
Optimization, 325
Ordered Weighted Av, 88
Ordinal Information, 284
Ordinal Regression, 178
Organizing Objectives, 151
Outer Approximation, 333
Outranking Methods, 68, 160, 163, 228,
272, 334, 337, 375
Outranking Relation, 278
Over-Fitting, 250
OWA Operator, 135
Point Estimates, 249
Polynomial Model, 183
Portfolio Optimization, 231, 298
Portfolio Selection, 55, 58, 184, 196, 363
Positional Voting Systems, 313
Positively Homogeneous Functions, 244
Possibilistic Framework, 252
Possibilistic Model, 310
Possibility Theory, 252
Power Generation, 232
Preference Aggregation, 118, 131, 226, 317
Preference Disaggregation, 79, 245, 278
Preference Information, 169, 201, 277, 308
Preference Learning, 102, 219, 222, 234,
245, 302, 366, 369
Preference Modeling, 157, 219, 278, 308,
336
Preference-Based Evolutionary Algorithms,
166, 291
Preferences, 87, 173, 200, 207, 215
Preferences And Utilities, 329
Premature Newborns Care, 368
Preservation Region, 305
Preventive Maintenance, 74
Price Volatility, 212
Pricing, 301
Principal Component Analysis, 307
Principle Of Tolerance, 350
Prioritisation Methods, 266
Prioritizing Objectives, 151, 221
Privacy, 243
Problem Structuring, 131, 148, 150, 260,
341
Product Development, 128
Production, 143, 248, 330
Production Cost, 232
Production Scheduling, 50, 74
Project Portfolio Selection, 60
Project Risk, 79
Project Scheduling, 235
PROMETHEE, 91, 163, 265, 312, 358,
371
Proper Epsilon-Efficiency, 137
Prospect Theory, 371
Provable Approximation Guarantees, 316
Proximity-Based Decision Rules, 308
Pseudo-Boolean Search Space, 361
Psychology, 107
Pairwise Comparison, 157, 172, 230, 321
ParadisEO-MOEO, 252
Parallel Algorithm, 281
Parallel Computing, 175
Parallel Multi-Objective Optimization, 295
Parallelism, 100
Parameter Precision, 249
Parameter-Dependent Problem, 360
Parametrization, 348
Pareto Dominance , 252
Pareto Equilibria, 296
Pareto Local Search Algorithm, 252
Pareto Optimal Investment Portfolio, 224
Pareto Optimal Solutions, 78, 125, 166,
253, 279, 323, 328, 355
Pareto Set, 255, 285
Partial Preorder, 334
Participatory Evaluation, 241
Participatory Multi-Criteria Evaluation,
288, 377
Path-Following Methods, 287, 360
Penalty And Reward Functions, 318
Penalty Methods, 170
Performance Measurement, 86, 175, 229,
353
Performance Targets, 318
PISA Survey, 228
Plant Capacity Planning, 119
Plaster Waste, 297
Plastic, 325
383
Public Policy, 77, 346
Public Safety, 68
Public Transport, 377
Robust Ordinal Regression, 178, 219, 220,
278, 322, 340, 358
Robust Supply Chains, 231, 327
Robustness Analysis, 278
Robustness And Sensitivity Analysis, 231,
324, 335, 340, 341
Rough Sets, 140
Routing, 75, 76
Rule Extraction, 218
Rule-Based Knowledge Acquisition, 218
Runtime Analysis, 361
Rural Planning, 162
Qualitative Multi-Criteria Model, 183, 317
Qualitative Reasoning, 127
Quality Indicators, 188
Quickest Flow, 261
Radial Projection, 305
Rank Reversal, 312
Ratio Analysis, 290
Ratio Of Directional Derivatives, 357
Reachability, 62
Real Options Analysis, 262
Recycling Facilities, 53
Redistribution Mechanisms, 130
Reference Hyperplane, 305
Reference Multifunction Method, 308
Reference Point, 99, 168, 169, 267, 276,
289–291, 316, 321, 349, 371
Reference Point based NSGA-II, 364
Regions, 354
Reinforcement Learning, 255
Relational Modeling, 317
Relative Importance, 357
Renewable Energy, 232
Replacement Cost, 356
Representation Quality, 211
Representative Set, 191, 211
Resampling, 168
Resort Development Investment, 356
Restoration, 67
Retailing, 377
Returns To Sclae, 305
Revealed Preferences, 212
Revenue Management, 301
Revenue-Risk Model, 329
Reverse Auctions, 236
Risk, 216, 275
Risk Analysis, 101, 141, 176, 263, 301,
319
Risk Assessment, 70
Risk Communication, 372
Risk Management, 241, 243, 370, 372
Road Safety, 158
Robust Elicitation, 321
Robust Optimization, 80
Safest Flow, 261
Satisfying Philosophy, 336
Savage’s Criteria, 224
Scalarization, 54, 56, 144
Scale Compatibility, 134
Scenario Planning, 194
Scenarios, 194, 230–232, 241, 279, 327
Scheduling, 54, 63, 144, 271
Scientific Uncertainty, 372
Scoring Rules, 313
Screening, 73
Semidefinite Programming, 89
Sensitivity Analysis, 53, 78, 94, 125, 146,
232, 237, 288, 305
Sensory Attributes Prediction, 250
Sequential Solution Approach, 213
Service Quality, 140
Set Covering Problem, 49
Shortest Path, 75, 175, 214, 247, 335
Simple Elitist Evolutionary Algorithm, 252
Simulated Annealing, 268
Simulation-Based Optimization, 99, 168
Simultaneous Solution Approach, 213
Singular Value Decomposition, 307
SIR, 350
Situational Judgment, 107
SMAA, 92, 340, 358
Smart Devices, 177, 232
Smart Home System, 352
Smart-Swaps, 134
Social Decision Making, 299
Socially Responsible Investment, 55, 126,
299, 349, 363
Soft-Computing, 184, 194
Software, 98, 230, 314
Software Protocol, 295
384
Solution Concepts, 136
Solution Diversity, 109
Southern Spain, 120
Spanning Tree, 124
Spatial Multi-Criteria Evaluation, 131, 206,
323, 345, 375
Stability Radius Of Portfolio, 224
State Of The Art, 202
Statistics, 132
Stochastic, 356
Stochastic Dominance Constraints, 320
Stochastic Knapsack, 351
Stochastic Linear Program, 339
Stochastic Multi-Objective Acceptability,
178
Stochastic Programming, 80, 320, 331
Stochastic Systems, 168
Stopping Rule, 188
Strategic Decisions, 341
Strategic Game, 296
Strategic Information Systems, 93
Strategic Planning, 77, 341
Structural Modeling, 268, 342
Structuring Decisions, 108, 151, 297
Substitutability between components, 276
Supervised Learning, 218
Supply Chain Management, 66, 93, 143,
193, 301
Supported Non-Dominated Solution, 211
Surrogate Modeling, 81, 132, 203
Surrogate Relaxation, 344
Sustainability, 59, 77, 93, 204, 239, 306,
330, 346, 353, 371
Sustainability Assessment, 104, 158, 280,
345, 353, 375
Sustainable Supply Chains, 59, 347
Synhronous Methods, 348
Synthetic indicators, 276, 299, 349
System Integration, 232
Systemic Decision Making, 350
Tabu Search, 95
Task Allocation, 257
Third Party Logistic Supplier, 303
Time Dependent Data, 261
Time Surplus, 133
Timetable Information, 247
Timetabling, 90
Tolerance Distribution, 350
385
TOPSIS, 86, 197, 262, 263
Town Regeneration, 377
Trade-Offs, 232
Traffic, 154
Train Networks, 247
Transaction Costs, 320
Transport Policy, 346
Transportation, 75, 116
Transshipment, 261
Travelling Salesman, 325
Triangular Fuzzy Numbers, 252
Triple Bottomline, 364
Two Types Of Error, 179
Two-Phase Method, 365
Uncertain Pareto Dominance, 203
Uncertainty, 53, 80, 252
Uncertainty Handling, 279, 288
Uncertainty Modelling, 193, 196, 208, 216,
237
Uranium Processing, 67
User Equilibrium, 133
UTADIS, 123, 246, 340
Utility, 185
Utility Theory, 101, 159, 215, 216
Value Focus Thinking, 131, 148, 150, 185,
198, 297
Value Function, 115, 373
Variable Reduction, 283
Variational Problems, 270
Vector Optimization, 137
Vehicle Routing Problem, 95, 252
Vehicular Ad Hoc Networks (VANET),
295
VIKOR, 112, 229, 367
Visegrad Four, 354
Visual Steering, 364
Vulnerability, 241
Waste
Water
Water
Water
Collection Problem, 76
Infrastructure Planning, 215, 237
Resources, 318
Resources Management, 149, 197,
237, 323, 326, 372
Web Architecture, 376
Weighted product, 86
Weighted Sum, 56, 146
Weights, 51, 215
Wellbeing, 61
WINGS, 342
Workforce Scheduling, 200
386
List of Authors
¨ orni, Anssi
O¨
MO3E, 51
¨
Ozpeynirci, Selin
FR3E, 246
C
¸ o¨l, G¨
ul¸cin
MO2E, 200
Altuzarra, Alfredo
MO5H, 350
Alves, Maria João
TU3F, 111
TU3G, 117
Amer, Noha
TH4F, 257
Andersen, Kim Allan
TH2B, 351
Andina, Diego
FR3G, 160, 162, 163
TH3G, 145, 337
Andrés Payán, Ana
MO5H, 53
Angilella, Silvia
TU2E, 280
TU4D, 92
Angulo-Guerrero, Mar´ıa José
TH4C, 276
Anic, Ivan
WE2B, 228
Antón, José
FR3G, 162
TH3G, 337
FR3G, 160, 163
TH3G, 145
Antunes, Paula
TU2B, 377
Aouni, Belaid
MO2E, 336
MO2B, 201
Arenas-Parra, Mar
TH2D, 64
TU2D, 195
Arriaza, Manuel
TH4B, 120
Autran M. Gomes, Luiz F.
FR3E, 123
WE1E, 306
Avinadav, Tal
TU5D, 301
Aydinbeyli, Yakup Emre
TH4F, 63
Azarm, Shapour
TU4D, 269
Abdelkader, Mendas
MO2D, 226
AbuDahab, Khalil
MO5H, 218
Afsordegan, Arayeh
TU5B, 127
Agell, N´
uria
TH3C, 245
TU5B, 127
Aguarón, Juan
MO5H, 300
Aguayo, Ernesto
TH3D, 82
Akyol, Emine
TU5E, 144
Al¸cada-Almeida, Lu´ıs
TH4E, 375
Alba, Enrique
MO5H, 166
WE2E, 295
Alcalá, Julián
TU5E, 268
Alegr´ıa Mej´ıa, Lol-chen
MO4F, 265
MO4F, 240
Alencar, Marcelo
WE2D, 275
WE2D, 297
TH2E, 207
TU4F, 130
Almeida, Adiel
WE2D, 275
Almeida-Filho, Adiel
WE2D, 74
Almulhim, Tarifa
WE2C, 110
Alonso, Pedro
WE1B, 338
387
Bäker, Bernard
TH3F, 343
B¨
using, Christina
TU3E, 316
Backovic, Marko
WE2B, 228
Baeza-Sampere, Ismael
TU4G, 126
WE1C, 310
Bahri, Oumayma
FR3D, 252
Bai, C. M.
TU5D, 66
Balaguer, Rosario
TU4G, 55
Bamford, David
TH4C, 353
Bana e Costa, Carlos
TH1A, 309
TU4E, 370
Banasik, Aleksander
TU3B, 239
TH4D, 227
Bandet, Jean
TU4C, 71
Barfod, Michael Bruhn
TU5B, 141
Batista dos Santos, Lucelina
MO4C, 374
Baucells, Manel
TU4E, 159
Bayazit, Ozden
TU3B, 303
Bazgan, Cristina
FR2E, 285
Beck, Joakim
TH3E, 132
Belmokaddem, Mustapha
MO2E, 129
Belton, Valerie
TU5C, 139
Ben Abdelaziz, Fouad
TH4F, 257
Ben Amor, Nahla
FR3D, 252
Berbel, Julio
MO5H, 212
Berm´
udez, José D.
WE1F, 196
Bernal, Astrid
FR2D, 368
Bertomeu Garc´ıa, Mercedes
MO3D, 210
Bertsch, Valentin
WE1D, 288
WE1D, 241
Bilbao, Amelia
TH2D, 64
TU2D, 195
Blanco, Victor
WE1G, 89
Boggia, Antonio
FR2C, 322
Bohanec, Marko
FR2G, 183
TU2B, 317
Borrion, Hervé
MO4G, 243
Bottero, Marta
TU2E, 280
TU3B, 345
Bouslah, Kais
MO2G, 73
Branke, Juergen
TU3F, 369
Brauers, Willem K. M.
MO2B, 290
Bravo Sellés, Milagros
TU4G, 299
Brison, Valerie
MO4F, 173
Brockhoff, Dimo
MO3F, 361
Brzostowski, Jakub
TH3D, 359
Buitrago, Eva
WE2G, 296
Buruk, Yeliz
TU3G, 54
Buxadé, Carlos
TU4C, 146
Córdoba, Mar´ıa-Adelaida
FR2D, 368
FR2F, 152
FR3C, 60
388
MO3D, 69
TU3C, 78
MO2G, 349
MO3F, 291
Cabrera G., Guillermo
TU5E, 244
Cadenas, José Manuel
WE1F, 184
Calvete, Herminia I.
TU3F, 83
Calvo, Clara
WE1F, 58
Camanho, Roberto
WE1E, 306
Camanho, Thomaz M.
WE1E, 306
´
Camino-Saco, Angeles
WE1B, 172
Campoy-Mu˜
noz, Pilar
TU2F, 153
Cano, Javier
MO4G, 101
TU2F, 323
Caraballo, M. Angeles
WE2G, 296
Casas Páez, Pedro Nicolás
WE1B, 128
Castillo, Mario
FR2D, 368
Cavalcante, Cristiano
WE2D, 74
Cekyay, Bora
TH4B, 96
Cerqueus, Audrey
MO4D, 344
Chaabane, Djamal
FR3C, 84
FR3D, 339
Chandra, Arjun
TU4F, 130
Chang, Chiao-Chen
TU4B, 356
Charpak, Nathalie
FR2D, 368
Chen, Chiau-Ching
TH4B, 94
Chen, Yu-Wang
MO4E, 121
MO4B, 250
MO5H, 218
WE2G, 108
Chen, Yue
MO4E, 121
Chern , Yuching
MO5H, 367
Chernonog, Tatyana
TU5D, 301
Cheung, Alan K. L.
MO2D, 75
Chiazzaro, Mauricio
TU2F, 323
Chikumbo, Oliver
MO4E, 364
Chin, Yang-Chieh
TU4B, 140
Chmielewski, Hana
FR2F, 109, 326
Chung, Eun-Sung
FR3F, 197
Church, Richard
TH2B, 214
Ciffroy, Philippe
WE2C, 72
Cimen, Emre
TH3C, 187
Cipriano Rodrigues, Teresa
MO3C, 260
TH3G, 145, 337
Cl´ımaco, João
WE1E, 204
TU3D, 242
TH4D, 227
TU3B, 239
Clemente, Thárcylla
WE2D, 275
Coelho Silva, Ricardo
TH2D, 332
Coenen, Thomas
TU2G, 251
Coll-Serrano, Vicente
TU4G, 126
WE1C, 310
Colombo, Federico
389
FR3G, 163
Colorni, Alberto
TH3D, 302
Comes, Tina
TU5D, 327
WE1D, 241
Corrente, Salvatore
TH3B, 272
TU4D, 92
WE2B, 358
FR2C, 322
TU2E, 280
TU3F, 369
Cortes Aldana, Felix Antonio
WE1B, 128
Cremades, Lázaro V.
TU5B, 127
Critto, Andrea
MO5H, 70
WE2C, 72
Cruz Corona, Carlos
TH2D, 332
TU2D, 194
Dehnokhalaji, Akram
WE1C, 305
Del Vasto, Luis
WE2B, 334
Dellnitz, Michael
TU2G, 360
Dias, Luis
FR2D, 234
TU2E, 220
TH4E, 375
Diaz-Madro˜
nero, Manuel
TH2D, 143
WE1F, 193
Dietermann, Ansgar
TH3F, 343
Dirks, Kim N.
MO2D, 75
Dopazo, Esther
TH2D, 118
Dosal Vi˜
nas, Elena
MO5H, 53
Drugan, Madalina M.
MO2F, 255
Duarte, Abraham
TU3D, 76
Dudas, Catarina
WE1D, 330
Duh, Kevin
MO5H, 254
Durillo, Juan J.
MO5E, 100
Dächert, Kerstin
TH2G, 105
D´ıaz-Balteiro, Luis
MO3D, 210
da Silva Vieira, Ricardo
TU2B, 377
Dall’O, Giuliano
TH3G, 77
Dandurand, Brian
TH2E, 164
De Armas, Jésica
TU2D, 256
de Castro Pardo, Mónica
MO2D, 304
De los R´ıos, Lisandro
FR3G, 163
De Schepper, Ellen
TU5C, 293
De Smet, Yves
WE2B, 312
TH4C, 158
Deb, Kalyanmoy
MO2F, 99, 168
MO4E, 364
WE1D, 330
Ehrgott, Matthias
TH2G, 191
MO2D, 75
TH2B, 351
TH4G, 48, 133
TU5D, 231
TU5E, 244
TU5F, 333
El badraoui, Khalid
MO2G, 73
El-Ghazali, Talbi
FR3D, 252
El-Haj Ben-Ali, Safae
WE1G, 89
Emelichev, Vladimir
MO2G, 224
Erb, Stephan
390
TU5G, 175
Ergu, Daji
FR2B, 142
Escobar, Mar´ıa Teresa
MO5H, 300
Escobar-Toledo, Carlos Enrique
MO4F, 240
MO4F, 265
Eskova, Olga
MO4G, 329
TU2C, 357
MO3D, 69
Gómez-Garc´ıa, Esteban
WE1B, 172
TH4B, 120, 156
Galé, Carmen
TU3F, 83
Galand, Lucie
WE2F, 135
Galbiati, Lorenzo
TU2F, 323
Gallina, Valentina
MO5H, 70
Gandibleux, Xavier
FR2E, 365
MO4D, 344
TH4G, 52
TU5C, 293
TU4G, 55, 299
Garc´ıa Pérez, Mar´ıa Dolores
WE2G, 223
Garc´ıa-Alonso, Carlos R.
TU2F, 153
Garc´ıa-Segura, Tatiana
TU5E, 268
Gargallo, Pilar
MO5H, 350
Garnevska, Elena
FR2D, 186
Garrido, Maria del Carmen
WE1F, 184
Gavranis, Andreas
WE2E, 233
Gaytán-Iniestra, Juan
TU3C, 78
Geiger, Martin Josef
FR3C, 116
Geldermann, Jutta
TH4E, 371
Genova, Krasimira
TH4D, 376
Gerdessen, Johanna
TH4D, 227
TU3D, 242
Ghaderi, Mohammad
TH3C, 245
Gila-Arrondo, Aránzazu
Farion, Ken
TH2C, 181
Faustino, Ana
FR3F, 318
WE2G, 180, 223
MO2F, 188
TU3E, 281
Ferreira, Rodrigo
WE2D, 74
Ferretti, Valentina
TU3B, 345
TU4C, 131
TU2E, 280
Fiala, Petr
WE1G, 167
Fichtner, Wolf
WE1D, 288
Figueira, Jose
TH4D, 259
WE2B, 358
Filipiˇc, Bogdan
TU3F, 203
Fraga, Eric
TH3E, 132
Fuentes Rojas, Ever Angel
MO5H, 325
Fulga, Cristinca
TU4G, 298
G¨
ul, Sait
WE1B, 112
Gómez, Carlos Mario
MO5H, 212
Gómez, Trinidad
FR3C, 60
391
TU3E, 281
Giménez, Juan Carlos
MO3D, 210
Ginestar, Concepcion
MO2D, 304
Gocuklu, Gulcan
TU3G, 54
Goetzmann, Kai-Simon
TU3E, 316
Goienetxea, Ainhoa
WE1D, 330
Goldsztejn, Alexandre
TH2F, 287
Gon¸calves, Marcos E.L.
FR3E, 123
González-Pachón, Jacinto
TU2B, 157
Goodman, Erik
MO4E, 364
Gorobetz, Mikhail
MO5H, 154
Gortázar, Francisco
TU3D, 76
Gouliashki, Vassil
TH4D, 376
Gourvès, Laurent
MO4D, 85
Granvilliers, Laurent
TH2F, 287
Grau, Juan
FR3G, 160, 163
TH3G, 145
FR3G, 162
TH3G, 337
Grebe, Tabea
TH2E, 307
Greco, Salvatore
FR2C, 322
TU2E, 178
TH3B, 219, 272
TU2E, 280
TU3E, 277
TU3F, 369
TU4D, 92
WE2B, 358
Guerrero-Baena, Mar´ıa Dolores
TH4B, 156
Gurevsky, Evgeny
TH4G, 52
Gurgel, André
WE2D, 68
Gutiérrez, César
MO4C, 137
Gutierrez-Martin, Carlos
MO5H, 212
Gutjahr, Walter
FR3D, 320
Hämäläinen, Raimo
MO3E, 134
Habenicht, Walter
MO2C, 106
Hakanen, Jussi
TU4F, 81
Handl, Julia
TH2E, 207
TH3C, 217
TU4F, 315
Hartikainen, Markus
MO4B, 282
Hasannasab , Maryam
TU5F, 333
Hasg¨
ul, Servet
MO2E, 200
Hazin, Luciana
WE2D, 297
Henggeler Antunes, Carlos
TU3G, 117
Henig, Mordecai
TU5D, 301
Hernández Jiménez, Mar´ıa Beatriz
MO4C, 171
Hernández, Elvira
MO4C, 125
Hernández-D´ıaz, Alfredo G.
TU3D, 76
FR2F, 152
Hernandez, Monica
MO3D, 69
´
Hinojosa, Miguel Angel
TU3D, 76
Hirschberger, Markus
MO2G, 363
Ho, Phong
TH4F, 62
Horenkamp, Christian
TU2G, 360
392
Hu, T. L.
TU5D, 66
Hu, Weiwei
TU4D, 269
Huang, Chi-Yo
TU4B, 177
Huang, Chung-An
TU3C, 352
Huber, Sandra
FR3C, 116
Huerga , Lidia
MO4C, 137
Huy, Truong
TH4F, 62
Jiménez, Bienvenido
MO4C, 137
John, Agah
TU4F, 315
Jones, Dylan
TU1A, 202
Juutinen, Artti
TH2F, 206
Köbis, Elisabeth
TH2F, 80
Kabak, Ozgur
TH4B, 96
Kadzinski, Milosz
MO5E, 278
TH3B, 340
Kahraman, Y.Rıza
MO3B, 57
Kaliszewski, Ignacy
MO3F, 192
Kamisli Ozturk, Zehra
MO3B, 238
Kandakoglu, Ahmet
MO3B, 57
TU5E, 86
Kandakoglu, Makbule
TU5E, 86
Kanellopoulos, Argyris
TU3B, 239
Kao, Yu-Sheng
TU4B, 177
Karakaya, G¨
ul¸sah
TU3G, 155
Karelkina, Volha
MO2B, 348
Karlsson, Alexander
MO2F, 168
Karpak, Birsen
TU3B, 303
TU5B, 59
WE1E, 347
TU3B, 93
Kasimbeyli, Refail
MO4B, 56
TH2B, 97
Keeney, Ralph
MO1A, 151
Keshvari, Abolfazl
WE1C, 283
Ichiro, Nishizaki
TU4E, 258
MO3C, 161
Ide, Jonas
TU5D, 231
Iranzo, José A.
TU3F, 83
Isern, David
TH4E, 366
Isigonis, Panagiotis
MO5H, 70
WE2C, 72
Ismaili, Anisse
MO4D, 124
WE2F, 135
Ivorra, Carlos
WE1F, 58
Jablonsky, Josef
TH2C, 314
Jamain, Florian
FR2E, 285
Javier, Fiallos
TH2C, 181
Jensen, Anders Vestergaard
TH4C, 346
Jermann, Christophe
TH2F, 287
Jiménez López, Mariano
TH2D, 64
TU2D, 195
Jiménez, Antonio
TH3D, 82
TU5C, 67
393
Keyhani, Mohammad Hossein
WE2F, 247
Kiczkowiak, Tomasz
MO3F, 192
Kirilov, Leoneed
TH4D, 376
Kirlik, Gokhan
TU5F, 147
Kiszova, Zuzana
WE2C, 354
Klamroth, Kathrin
TH2F, 80
MO4B, 282
TH2G, 105, 362
TH4D, 259
Knowles, Joshua
TH2E, 207
TU4F, 315
Ko, Yu-Chien
WE2C, 61
Ko¸canli, M¨
uzeyyen Melek
TH4F, 63
Kokko, Tommi
TU4F, 81
Koksalan, Murat
MO2C, 114
FR3C, 189
FR3E, 246
MO2C, 115
MO4B, 113
TU3G, 155
Korhonen, Pekka
WE1C, 283
MO2C, 114
MO3E, 51
Korotkov, Vladimir
MO2G, 224
Kou, Gang
FR2B, 45, 142
Kozanidis, George
WE2E, 233
Krampf, Peter
MO3G, 185
Krieter, Joachim
TU4C, 146
Krupi´
nska, Katarzyna
FR2G, 87
Krym, Eduardo
WE2D, 275
Krzeszowska, Bogumila
TU4D, 235
Kuhn, Kenneth
WE2F, 335
Kunz, Reinhard
MO3G, 148
MO3G, 150
Kuo, R. J.
TU5D, 66
López Agudo, Luis Alejandro
MO2B, 289
López Montoya, Rubén
MO4C, 125
López-Redondo, Juana
MO2F, 188
TU3E, 281
López-Sánchez, Ana Dolores
TU3D, 76
Laguna, Manuel
FR2F, 152
Lahtinen, Tuomas
MO3E, 134
Lamata, Mar´ıa T.
TU2D, 194
Lami, Isabella
TU2E, 280
Lancinskas, Algirdas
WE2G, 180
Langhans, Simone
MO2D, 149
TU5C, 208
Laryea, Rueben
FR3E, 79
Lauven, Lars-Peter
WE1D, 119
Lavygina, Anna
TH2E, 207
Le Sage, Tanya
MO4G, 243
Lee, Boram
FR3F, 197
Leleur, Steen
TH4C, 346
Leon, M. Amparo
MO3D, 69
Lerche, Nils
TH4E, 371
394
Levchenkov, Anatoly
TH4F, 271
Lewis, Peter
TU4F, 130
Lienert, Judit
MO2D, 149
FR3F, 237
TU3G, 215
TU5C, 208
Liern, Vicente
FR3C, 60
WE1F, 58
Lilti, Jean-Jacques
MO2G, 73
Lin, Changsheng
FR2B, 45
Lin, T. Tyrone
TU4B, 356
Lin, Yi-Chun
TH4B, 94
Liu, Jinyan
FR2C, 321
Liu, Shuang
FR3E, 372
Lizin, Sebastien
TU5C, 293
Llamazares, Bonifacio
TH2C, 313
Logist, Filip
TU2G, 251
Lokman, Banu
FR3C, 189
MO2C, 114, 115
Long, Pham
MO3C, 236
Lopes, Diana F.
TU4E, 370
Lozano, Carlos A.
FR3B, 263
Lu, Ming-Tsang
TU4B, 65
Luque, Mariano
MO2B, 289
MO3F, 291, 355
MO5H, 166
TH4C, 276
TU3E, 169
Lust, Thibaut
FR2E, 199
M’Zali, Bouchra
MO2G, 73
TU4G, 126
Mönkkönen, Mikko
TH2F, 206
MO5H, 136
WE2G, 296
MO2G, 349
TU4G, 55, 126
Maag, Volker
TH2E, 307
Macedo, Marcelo G. C.
WE1E, 306
Macharis, Cathy
MO4F, 91
Machuca, Enrique
WE2F, 274
Maheshwari, Vaibhav
TH3E, 249
TU5F, 213
Malecki, Jozef
TH3E, 122
Mandow, Lorenzo
TU5G, 328
WE2F, 274
Manel, Maamar
WE2E, 264
Manotas , Diego F.
FR3B, 263
FR3B, 262
Manotas , Luis A.
FR3B, 262
´
Marcenaro-Gutiérrez, Oscar
MO2B, 289
Marcomini, Antonio
MO5H, 70
WE2C, 72
Marin, Lucas
TH4E, 366
Marins, Fernando
WE1B, 338
MO2D, 304
MO3D, 273
Marqués, Inmaculada
395
MO3D, 311
Marr, Norman
FR2D, 186
Mart´ın Fernández, Paula
TU4C, 146
Mart´ın, Jacinto
MO5H, 47
Mart´ın, Miguel C.
TU5C, 67
Mart´ınez Gamboa, Jeyson Andres
MO5H, 325
MO2F, 188
TU3E, 281
Martin, Benjamin
TH2F, 287
TU5C, 293
Martin, Rodrigo
FR3G, 160
Martinez, Raquel
WE1F, 184
Martins, Luiz Geraldo Biagioni
WE1E, 306
Masakazu, Ohmi
TU4E, 258
Masegosa, Antonio D.
TU2D, 194
Mason, Andrew
TU5E, 244
Massei, Gianluca
FR2C, 322
WE1E, 225
Matarazzo, Benedetto
TU3E, 277
Mateo, Manuel
TH4G, 50
Mateos, Alfonso
TH3D, 82
TU5C, 67
Matuschke, Jannik
TU3E, 316
Mazurek, Jiˇr´ı
TU3C, 284
WE2C, 354
Mazziotta, Adriano
TH2F, 206
McGillis, Sheila
MO2B, 201
Mebrek, Fatma
FR3D, 339
Medrano, F. Antonio
TH2B, 214
Mej´ıa-Argueta, Christopher
TU3C, 78
Melián, Belén
TU2D, 256
Melloul, Sakina
MO2E, 129
Merel, Aurélien
TH4G, 52
Michalowski, Wojtek
TH2C, 181
Michnik, Jerzy
FR2B, 342
Miettinen, Kaisa
MO3E, 279
MO4B, 282
TH2E, 102
TH2F, 206
TU2G, 253
TU3E, 169
Mikhailov, Ludmil
MO3B, 266
WE2C, 110
Milan, Lauriane
MO4F, 91
Mileva-Boshkoska, Biljana
FR2G, 183
Miroforidis, Janusz
MO3F, 192
Mitchener-Nissen, Timothy
MO4G, 243
Mitsuhiro, Imai
MO3C, 161
Mlakar, Miha
TU3F, 203
FR2F, 152
MO3D, 69
TU3C, 78
Molines, Nathalie
TU4C, 71
Mondini, Giulio
TU3B, 345
Monnot, Jérôme
MO4D, 85
396
Monroy, Luisa
MO5H, 136
WE2G, 296
Montero, Javier
FR3G, 138
Montibeller, Gilberto
TU2C, 341
TU4C, 131
Moradi, Siamak
TH4G, 48
Moreno Pérez, José A.
TU2D, 256
Moreno, Antonio
TH4E, 366
Moreno-Jiménez, José Mar´ıa
MO5H, 300
MO5H, 350
Mosquera, Alejandro
WE1B, 172
Mota, Caroline
WE2D, 68
WE2D, 297
Mouslim, Hocine
MO2E, 129
Mousseau, Vincent
FR2C, 222, 321
FR3E, 246
TH3B, 219
WE2E, 264
Muetterlein, Joschka
MO3G, 150
Mula, Josefa
TH2D, 143
WE1F, 193
Mustajoki, Jyri
MO3E, 279
Myers, Chris
MO4G, 176
Nevima, Jan
WE2C, 354
Ng, Amos
WE1D, 330
MO2F, 99, 168
Nguyen, Viet Hung
MO4D, 88
Nielsen, Lars Relund
TH2B, 351
Nikulin, Yury
MO2B, 348
Nisel, Rauf
TU3C, 229
Nisel, Seyhan
TU3C, 229
Norese, Maria Franca
TH3G, 77
Novello, Chiara
TH3G, 77
Novo, Vicente
MO4C, 137
Nowé, Ann
MO2F, 255
Nowak, Maciej
MO2C, 331
Nowak, Uwe
TH2E, 307
Ober-Blöbaum, Sina
TU2G, 360
Ocampo, Germán
FR3B, 262
Ogryczak, Wlodzimierz
WE1G, 324
Ojalehto, Vesa
TH2E, 102
Oliveira, Gabriela
FR2D, 234
MO3C, 260
TU4E, 370
Onsel Ekici, Sule
TH4B, 96
MO4C, 171, 374
Ouerdane, Wassila
FR2C, 321
WE2E, 264
Ozaydin, Ozay
Nasrabadi, Nasim
WE1C, 305
Ndiaye, Ismaila Abderhamane
TU5G, 261
Nejc, Trdin
TU2B, 317
Nekhay, Olexandr
TH4B, 120
Neron, Emmanuel
TU5G, 261
397
TH4B, 96
Ozcetin, Erdener
TH3F, 95
Ozpeynirci, Ozgur
MO2C, 115
Ozturk, Gurkan
TH2B, 97
TH3C, 187
TH3F, 95
Peng, Hsin-Chuan
TU4B, 65
Peng, Kua-Hsin
TU4B, 65
Peng, Yi
FR2B, 45
Perederieieva, Olga
TH4G, 133
Perny, Patrice
WE2F, 135
Perzina, Radomir
MO3B, 230
Petelin, Dejan
TU3F, 203
Pinheiro, Mariana
TH4E, 104
Piper, Brian E.B.
FR2F, 109
Pirlot, Marc
FR2C, 222
MO4F, 173
Pla Santamaria, David
TU4G, 299
Podkopaev, Dmitry
TH2F, 206
TH2E, 102
Ponte, An´ıbal
TH2G, 190
Popovic, Zoran
WE2B, 228
Portait, Roland
TH3G, 337
Pretolani, Daniele
TH2B, 351
Prevost, Aurélie
TU4C, 71
Przybylski, Anthony
FR2E, 365
MO4D, 344
Puerto, Justo
WE1G, 89
Pulido, Francisco Javier
TU5G, 328
Pérez, Carlos Javier
MO5H, 47
Pérez, Fátima
FR3C, 60
Pérez-de-la-Cruz, José L.
TU5G, 328
Pérez-Gladish, Blanca
MO2G, 349
TU4G, 55
TU4G, 299
Pérez-Moreno, Salvador
TH4C, 276
Pérez-Rodr´ıguez, Fernando
WE1B, 172
Pérez-Sánchez, Danyl
TU5C, 67
Pérez-Salas, José Lu´ıs
MO3D, 311
Paoloni, Daniele
WE1E, 225
Papamichail, K.Nadia
TH3C, 217
Paquete, Luis
TH2G, 190
TH2G, 362
Parchment, Avonie
TU2C, 107
Patrick, Jonathan
TH2C, 181
Pearce, Jon
MO2D, 75
Pearman, Alan
MO3C, 205
Peidro, David
TH2D, 143
WE1F, 193
Pelegr´ın, Blas
WE2G, 223
WE2G, 180
Quoc, Ho
TH4F, 62
R´ıos Insua, David
MO4G, 101
398
R´ıos, John-Jairo
FR2D, 368
Raith, Andrea
WE2F, 335
TH4G, 48, 133
Ram´ırez, Bárbara
MO4F, 240
Ramik, Jaroslav
MO3B, 230
Rangaiah, Gade Pandu
TH3E, 249
TU5F, 213
Rangel, Luis A.D.
FR3E, 123
Ranjithan, S. Ranji
FR2F, 109, 326
Ray, Duncan
MO3D, 273
Redchuk, Andrés
TU2F, 323
Reichert, Peter
TU5C, 208
MO2D, 149
TU4C, 216
Reinerman-Jones, Lauren
TU2C, 107
Reunanen, Pasi
TH2F, 206
Ribas, Jose Roberto
FR3F, 319
MO4E, 221
TH4E, 104
Rocchi, Lucia
WE1E, 225
Rocha, Mariana
MO4E, 221
Rodr´ıguez D´ıaz, Beatriz
TH4C, 276
Rodr´ıguez-Ur´ıa, Mar´ıa Victoria
TH2D, 64
Rodriguez, Tinguaro
FR3G, 138
Rojo-Alboreca, Alberto
WE1B, 172
Rolland, Antoine
FR2E, 199
Romero, Carlos
TU2B, 157
Rong, Aiying
TH4D, 259
Roszkowska, Ewa
TH3D, 359
Rubiales , Victoriana
MO5H, 136
Rubio, José Luis
FR3G, 162
Rufián-Lizana, Antonio
MO4C, 374
Rufo, Mar´ıa Jes´
us
MO5H, 47
Ruiz, Ana Belén
MO3F, 291, 355
TU3E, 169
Ruiz, Francisco
MO2G, 349
MO3F, 291
TU3E, 169
Ruiz, Francisco Javier
TH3C, 245
Ruiz, Juan-Gabriel
FR2D, 368
Ruiz-Garzón, Gabriel
MO4C, 374
MO4C, 171
Ruiz-Tagle, Mauricio
TH2D, 118
Sánchez, Maria
TH3G, 337
Sánchez-Guerrero, Gabriel
MO5H, 165
Saaty, Thomas L.
FR2B, 142
Sabin, Andrada
WE2G, 108
Saborido-Infantes, Rubén
MO5H, 166
MO3F, 355
Salazar-Ordo˜
nez, Melania
TU2F, 153
Salda˜
na, Alberto Carlos
WE1F, 184
Saleh, Khaled
TU4D, 269
Salling, Kim Bang
TU5B, 141
Salomon, Valerio
399
WE1B, 338
Salvador, Manuel
MO5H, 350
Samavedham, Lakshminarayanan
TH3E, 249
Sanchez, Monica
TH3C, 245
TU5B, 127
Sankaran, Baskaran
MO5H, 254
Sara¸c, Tugba
TH4F, 63
TU5E, 144
Sarabando, Paula
FR2D, 234
TU2E, 220
Sarin, Rakesh
TU4E, 159
Sarkar, Anoop
MO5H, 254
Sarkisyan, Rafael
MO4G, 329
TU2C, 357
Sarrazin, Renaud
TH4C, 158
Savan, Emanuel
MO4B, 250
Sayin, Serpil
TU5F, 211
TU5F, 147
Schätter, Frank
TU5D, 327
Schöbel, Anita
TH2F, 80
TU5D, 231
WE2F, 335
Schenker, Sebastian
WE1G, 98
Schmidt, Marie
WE2F, 335
Schnee, Mathias
WE2F, 247
Scholten, Lisa
FR3F, 237
Schulze, Britta
TH2G, 362
Schuwirth, Nele
TU4C, 216
TU5C, 208
Segura Garc´ıa del R´ıo, Baldomero
MO3D, 311
Segura, Marina
MO2D, 304
MO3D, 273
Semenzin, Elena
MO5H, 70
WE2C, 72
Sevaux, Marc
FR3C, 116
FR3F, 319
MO4E, 221
TH4E, 104
Shalchian, Homayoon
MO2G, 73
Shao, Lizhen
TH2G, 191
Sharma, Shivom
TU5F, 213
Shi, Yong
MO2E, 179
Shih, Hsu-Shih
TH4B, 94
Shukla, Kalpana
TU4D, 270
Siddique, Muhammad Imran
FR2D, 186
Siebert, Johannes
MO3G, 198
MO3G, 148, 150, 185
Siegmund, Florian
MO2F, 99, 168
Silva, Sandra
TH4E, 375
Silvennoinen, Kari
MO3E, 51
Sindhya, Karthik
MO5E, 209
TU3E, 169
TU4F, 81
Siskos, Yannis
TU2E, 178
Skocdopolova, Veronika
FR3B, 90
Skulimowski, Andrzej M.J.
FR2G, 308
400
Slowinski, Roman
TH3B, 219
FR2C, 322
TH3B, 272
TU2E, 178
TU3E, 277
TU3F, 369
WE2B, 334
Sobrie, Olivier
FR2C, 222
Sohler, Flavio
FR3F, 319
Soleimani-Damaneh, Majid
TH2C, 373
WE1C, 305
Soletti, Alfredo
FR3G, 160
Sousa, Victor
FR3F, 318
Soylu, Banu
TH2B, 49
WE1C, 103
Spanjaard, Olivier
WE2F, 135
Stamati, Ioanna
TU2G, 251
Stamenkovic, Mladen
WE2B, 228
Stamm, Christian
TU4C, 216
Staykov, Boris
TH4D, 376
Steponavice, Ingrida
TU2G, 253
Steuer, Ralph E.
MO2G, 363
Stewart, Theodor
TU2F, 267
MO3E, 279
Stidsen, Thomas
TU4F, 294
Stiglmayr, Michael
TH2G, 190
Stiller, Sebastian
TU3E, 316
Sureeyatanapas, Panitas
TH4C, 353
Syeda, Darakhshan Jabeen
TH3F, 170
Tammer, Christiane
TH2F, 80
Tanaka, Tamaki
TH2F, 292
Tanino, Tetsuzo
TH2F, 292
Taraszewski, Steve
TU3B, 93
Tarquis, Ana
FR3G, 160, 162, 163
TH3G, 145, 337
Tedeschi, Alessandra
MO4G, 101
Teghem, Jacques
TH4G, 50
Teich, Jeffrey
MO3C, 236
Telen, Dries
TU2G, 251
Teo, Grace
TU2C, 107
Tervonen, Tommi
TH3B, 340
Tezcaner, Diclehan
MO4B, 113
Tikkanen, Olli-Pekka
TH2F, 206
Timmermann, Robert
TU2G, 360
Tlilane, Lydia
MO4D, 85
Tomohiro, Hayashida
MO3C, 161
TU4E, 258
Topcu, Ilker
MO3B, 57
WE1B, 112
Torresan, Silvia
MO5H, 70
Toubaline, Sonia
MO4G, 243
Toutouh, Jamal
WE2E, 295
Trujillo, Tammy
FR2D, 368
Trzaskalik, Tadeusz
MO2C, 331
401
TU4D, 235
Tsoukiàs, Alexis
TH3D, 302
Tuˇsar, Tea
TU3F, 203
Turhan, Ugur
MO4G, 101
Tzeng, Gwo-Hshiung
TU4B, 65, 177
MO5H, 367
WE2C, 61
MO4F, 91
Vetschera, Rudolf
TU2E, 220
Vidal, Carlos
FR3B, 262, 263
Viguri Fuente, Javier
MO5H, 53
Villacorta, Pablo
TU2D, 194
Villanueva-Rodr´ıguez, Anastasio
TH4B, 120
Vincent, Thomas
FR2E, 365
TU5C, 293
Vitoriano, Bego˜
na
FR3G, 138
TU3C, 78
Udias, Angel
TU2F, 323
Ulengin, Fusun
TH4B, 96
Urenda Moris, Matias
WE1D, 330
Utz, Sebastian
MO2G, 363
Wachowicz, Tomasz
TH3D, 359
Wallenius, Hannele
MO3C, 236
Wallenius, Jyrki
MO3E, 51
MO2C, 114
MO3C, 236
Wang, Bo
MO2E, 179
Wang, Judith Y. T.
MO2D, 75
TH4G, 133
Wen, Hsin-Hui
WE2C, 61
Weng, Paul
MO4D, 88
Wiecek, Margaret
TH2E, 164
Wilson, Reginald
TU3D, 232
Wimmer, Maximilian
MO2G, 363
Won, Kwang-Jae
FR3F, 197
Wong, Wai Keung
MO4E, 248
Valle, Rogerio
WE1E, 204
Vallerio, Mattia
TU2G, 251
Valls, Aida
TH4E, 366
WE2B, 334
Van Den Honert, Rob
TU5B, 141
van der Vorst, Jack
TU3B, 239
Van Impe, Jan
TU2G, 251
Van Moffaert, Kristof
MO2F, 255
Van Passel, Steven
TU5C, 293
Vanderpooten, Daniel
FR2E, 285
Vaz, Daniel
TH2G, 190
Vercher, Enriqueta
WE1F, 196
Verdegay Galdeano, José Luis
TH2D, 332
Vergara-Maldonado, E. Ivonne
MO5H, 165
Verlinde, Sara
Xianhua, Wei
MO2E, 179
Xie, Naiming
402
MO3C, 205
Xu, Ling
MO3E, 182
WE2G, 108
MO4B, 250
MO4E, 121
MO5H, 218
WE2C, 110
Yılmaz, Hafize
WE1B, 112
Yamada, Syuuji
TH2F, 292
Yang, Jian-Bo
MO3E, 182
MO4B, 250
MO4E, 121
TH4C, 353
Yepes, V´ıctor
TU5E, 268
Yildiz, Gazi Bilal
WE1C, 103
Zabeo, Alex
MO5H, 70
WE2C, 72
Zahedi, Siamak
TU5B, 127
Zaroliagis, Christos
TU5G, 174
Zheng, Jun
TU3G, 215
Zielniewicz, Piotr
WE2B, 334
Zietsman, Joe
TH4C, 346
Zilinskas, Antanas
TH4D, 286
Zilinskas, Julius
TH4D, 286
WE2G, 180
403

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