here

Multi-agent simulation for decision making
in distributed contexts
Yacine Ouzrout
LIESP/CERRAL Laboratory
University Lyon 2
France
e-mail: [email protected]
Plan
1. Introduction
Supply Chain Context
Supply Chain Management
Collaboration and Decision Making
2. Multi Agent Systems and Collaboration
Intelligent Agents
Multi Agent Systems
The use of MAS in SC context
3. Research topics & case studies
4. Conclusion
Supply Chains…
consist in a number of organizations which, combined,
deliver products and services that are valued by customers
contain organizations that can have differing and
sometimes, conflicting objectives
exist on a local, national, and global scale
can be simple or complex depending on factors such as
product design, lifecycle, supply and demand variability,
operational requirements, as well as many other factors.
Supply chain is a network
Wholesale
Distributors
Suppliers
Retailers
Manufacturers
Supplier
Exchanges
Customers
Logistics
Exchanges
Customer
Exchanges
Virtual
Manufacturers
Contract
Manufacturers
Logistics
Providers
Information Flows
Goods Flow
Supply Chain Management
addresses organizational decisions at the strategic, tactical,
and operational levels
extends beyond internal functions to incorporate a process
orientation across organizations in the supply chain
to be successful, requires that all individuals in the
organization adopt a similar orientation toward business
has, and will continue to provide a substantial contribution
to productivity on a global scale.
Supply Chain Coordination
Coordination
Coordination
Coordination
Mangmnt Mangmnt Mangmnt
LIVR. PURCH.
DELIV
APPRO. PROD
FAB. DISTRI
Supplier of
the supplier
external
supplier
Mangmnt
PURCH.
Mangmnt
PROD.
Company
Mangmnt
Coordination
Mangmnt Mangmnt Mangmnt
PURCH.
DISTRIB.
APPRO.PROD
FAB. DISTRI
LIVR. PURCH.
APPRO.
LIVRAISON
external
customer
Customer of
the customer
Information systems in extended SC
APS
Strategic
PLM
SRM
CRM
G.P.A.OERP
epr
oc
u
CP rem
en
FR
t
Planning
merce
e-com
B2C
B2B /
Operational
MES
Supplier
Manufacturer
SCE
WMS TMS YMS
Distributor
Retailer
• Customers Relationship Management, Supplier Relationship
Customer
Chain Execution
• Supply
Manufacturing
Execution
Systems:
Detail
scheduling,
Resource
allocation,…
Advanced
Systems:
Simulation
& Planning
• Product Planning
Lifecycle
Management:
Managing
all
the
information
Management:
Sales
force
management,
Marketing
strategies
• WarehousingSC
Management
Systems, Transport Management Systems,
optimization,
Flows
synchronization,…
about the products throughout their full lifecycle.
Yard Management Systems
Collaboration and new business processes
Firms need to consider the interactions with the suppliers and
the customers, and incorporate them into their decision making
process.
As companies outsource more and more of their current in
house activities, they will have to develop the software tools to
control and collaborate with their outsourcers.
Evolution of IT & the use of Internet technology have
drastically changed our way of doing business.
Business processes evolution
Intelligent Agents
The integration of the different IS would not solve the problem
and there is a need for an approach for modeling and analysis of
among enterprises and within the departments of an enterprise.
Such an approach would enable integrated SC decision-making.
An agent base framework can be the solution to address this critical
need.
One of the main benefits of using agent technology for SCM is the use of
the capacity of reasoning, collaborating, negotiating, sharing Information
of the intelligent agents.
Artificial Intelligence and Collaboration
Decision-Making
An agent has the following properties :
Autonomy: it should have some control over its actions and
should work without human intervention.
Social ability: it should be able to communicate with other
agents.
Reactivity: it should be able to changes its environment.
Pro-activeness: it should also be able to take initiative
based on pre-specified goals.
The use of MAS in SC context
A Multi-Agent System (MAS) is an organization of agents, interacting
together to collectively achieve their goals
Coordination: one issue on agent-based SCM is dedicated to
coordination. Some works are presented to develop collaborative SC
system, negotiation mechanisms, customer relationship .
Simulation: another issue concerns the agent-based simulation of SC
which try to show how agent-based supply chains can gain visibility
and efficiency through simulation under various strategies.
Design: some of the related agent based works are interested in how
supply chains be formed dynamically.
Knowledge Management: a last issue concerns the use of software
agents for knowledge management within a SCM context.
Example of Simulation Architecture
Agent
MAS Level
A
A
A
Object Data
Base
BD
Agent’s
interaction
Data
Exchange
Manufacturing
Model
Resources
Scheduling
& Planning
“Turbix” Case Study
ASC
2C
AS
2
ASC
1C
AS
1
AWC
1C
AW
1
Turbix
Raw Materials
Product
Component
Final
Product
AWC
2C
AW
2
ACC
1C
AC
1
ACC
2C
AC
2
Plan
1. Introduction
Supply Chain Context
Supply Chain Management
Collaboration and Decision Making
2. Multi Agent Systems and Collaboration
Intelligent Agents
Multi Agent Systems
The use of MAS in SC context
3. Knowledge Management in SC
Knowledge Exchange
Knowledge Classification
Knowledge Dynamic
Knowledge Representation
4. Conclusion
What is an intelligent agent
An intelligent agent is a system that:
• perceives its environment (which may be a collection of other agents, the
physical world, a user via a graphical user interface, the Internet, or other
complex environment);
• reasons to interpret perceptions, draw inferences, solve problems, and
determine actions;
• acts upon that environment to realize a set of goals or tasks for which it was
designed.
input/
sensors
Other Agents/
Environment
output/
effectors
Intelligent
Agent
What an intelligent agent can do
An intelligent agent can :
• collaborate with other agents to improve the accomplishment of their
tasks;
• carry out tasks on user’s behalf, and in so doing employs some
knowledge of the user's goals or desires;
• monitor events or procedures for the user;
• advise the user on how to perform a task;
• help different users collaborate.
Characteristic features of intelligent agents
Knowledge representation and reasoning
Ability to communicate
Exploration of huge search spaces
Use of heuristics
Reasoning with incomplete or conflicting data
Ability to learn and adapt
Knowledge representation and reasoning
• An intelligent agent contains an internal representation of its external application
domain, where relevant elements of the application domain (objects, relations,
classes, laws, actions) are represented as symbolic expressions.
• This mapping allows the agent to reason about the application domain by
performing reasoning processes in the domain model, and transferring the
conclusions back into the application domain.
ONTOLOGY
OBJECT
SUBCLASS-OF
represents
If an object is on top of
another object that is
itself on top of a third
object then the first object
is on top of the third
object.
Application Domain
BOOK
CUP
TABLE
INSTANCE-OF
CUP1
ON
BOOK1
ON
TABLE1
RULE
∀ x,y,z ∈ OBJECT,
(ON x y) & (ON y z) (ON x z)
Model of the Domain
Separation of knowledge from control
Implements a general method of interpreting the
input problem based on the knowledge from the
knowledge base
Intelligent Agent
Information /
Knowledge
other agents/
environment
Problem Solving
Engine
Knowledge Base
Information /
Ontology
Knowledge
Rules/Cases/Methods
Data structures that represent the objects and their relations (ontology)
from the application domain, general laws governing them,
action that can be performed with them, etc.
Agent architecture
• To enable agents to collaborate and make decisions, we must make
assumptions about how their decisions can be influenced
Collaboration
Communication
Information
Perception
Negotiation
Rationalities &
Goals
Knowledge Base
Case-Based Reasoning
Agent architecture (cont.)
Reasoning
. define_r()
. decision()
Design
. control()
Identification
. agent_id()
. analyze()
Perception
. wait_inf()
. get_inf()
Knowledge
. skills
. commitments
Rationalities
Intentions
Messages
. create_msg()
Communication
. send_msg()
. wait_msg()
. get_msg()
Simulation Scenarios
Scenario 1
ASC
2C
AS
2
“Customer Satisfaction”
ASC
1C
AS
1
AWC
1C
AW
1
AWC
2C
AW
2
ACC
1C
AC
1
Order
(Q, P, D)
Turbix
New plan
(Ortems)
Raw Materials
Product
Component
Final
Product
Simulation Scenarios
Scenario 2
ASC
2C
AS
2
“Supplier Exchange”
ASC
1C
AS
1
AWC
1C
AW
1
AWC
2C
AW
2
ACC
1C
AC
1
Order
(Q, P, D)
Turbix
negotiation
Raw Materials
Product
Component
Select
Supplier
Final
Product
Simulation Scenarios
Perform ance Indicators
Scenario 1
20
15
“Customer Satisfaction”
Finished
Products
10
Rubbishes
5
0
Workforce
1
3
5
7
9
11
Sim ulations
Scenario 2
5000
4000
Products
“Supplier
Exchange”
Supplier Exchange
3000
Supplier SC1
Supplier SC3
2000
1000
0
Week
“Turbix” Case Study application
User Interface
(Java)
Multi-Agent Level (JADE)
Agent belief, desire, intention
A set of commitment rules determines how the agent acts.
Each commitment rule contains a message condition, a mental
condition and an action.
In order to determine whether such a rule fires, the message
condition is matched against the message the agent has received
and the mental condition is matched against the beliefs of the
agent. If the rule fires, the agent becomes committed to
performing the action.
Agent belief, desire, intention
action request to B from A
B accepts or reject A’s request
If accepted, B attempts to perform A’s
request
Agent A
Environment
message of success or failure
Agent B
Agent communication
three aspects to the formal study of communication:
• syntax, how the symbols of communication are structured
• semantics, what the symbols denote
• pragmatics, how the symbols are interpreted
Global Dimensioning Decision Scenario
Supplier––RM
RM==BB
Supplier
Customer22
Customer
Customer11
Customer
Distributor
Distributor
Manufacturer- -PIPI
Manufacturer
Supplier––RM
RM==AA
Supplier
Warehouse1
Warehouse1
Manufacturer- -PP
Manufacturer
Supplier––RM
RM==CC
Supplier
Warehouse22
Warehouse
Customer22
Customer
Customer11
Customer
Mathematical Formulation
S1n
n
S2
S3n

...
Sn
 Ln
... S11 W11 ... W1p C11 ... C1m

... S21 W21 ... W2p C21 ... C2m
... S31 W31 ... W3p C31 ... C3m

... ... ... ... ... ... ... ...
m
... S1L1 WL11 ... WLpp C1L1 ... CLm

Define : Supply Chain Matrix ,
Costs , Variables and capacities
Added
AddedCost
Cost
AS C
AS2C2
AS C
AS1C1
OS C
OSiCi
OM C
OMiCi
AW C
AW1C1
Interaction
Interaction
Costs
Costs
TW C
TWiCi
AW C
AW1C1
DC C
DCiCi
AC C
AC1C1
AC C
AC2C2
DC C
DCiCi
TM C
TMiCi
PMC
PMC
PS
PS
Production
ProductionCost
Cost
Raw
Materials
Product
Component
SS C
SS2C2
SS C
SS1C1
Final Product
StorageCost
StorageCost
SW C
SW1C1
SW C
SW2C2
SC C
SC1C1
SC C
SC2C2
L2− > p
L2− > (m − 1)
L2− > p
L2− > p
L2− > (m − 1)
L2− > n
L2− > n
L2− > n
+
C
+
AW
C
+
ACiC
AS
Cost = ∑ OSiC + OMC + TMC + ∑ TWiC +
DC
C
+
SS
C
+
SW
C
∑
∑
∑
∑
∑
∑
i
i
i
i
i
i =1
i =1
i =1
i =1
i =1
i =1
i =1
i =1
Benefits of MAS for SC
The MAS clearly identifies the differing actor’s roles, functions,
knowledge,…
The MAS can model the distributed decision making processes.
The agents may dynamically respond to change, coordinating
their responses with other agents.
The simulation models describe the dynamic, the behavior, and
the interactions within the SC
Plan
1. Introduction
Supply Chain Context
Supply Chain Management
Collaboration and Decision Making
2. Multi Agent Systems and Collaboration
Intelligent Agents
Multi Agent Systems
The use of MAS in SC context
3. Research topics & case studies
4. Conclusion
Research projects
1. Multi-Agent architecture for SCM
Copilotes 2 Project :
« Trust » in Supply Chain
. Simulation model of trust
. Collaboration with a human
science Lab.
Logistic Cluster Project :
« Sustainable & Green SC »
. A Knowledge Management
system to manage the
Reverse Logistic
. PhD Thitiya
Research projects
1. Multi-Agent architecture for SCM
Copilotes 2 Project :
« The Beer Game »
Logistic Cluster Project :
« Serious game for logistics »
. Simulation model SC
collaboration
. Pedagogical tool.
. Simulation model of
logistic infrastructure
. Collaboration with logistics
companies
Research projects
2. Information System Interoperability
Project ANR:
« MAS & SOA »
. Service Oriented
Architecture
. Definition of a new model of
services based on
intelligent agents
. Collaboration with a Lab.
(>1 year)
Project e-tourism:
« Distributed KMS for e-tourism »
. Definition of a MA architecture
to manage the KM.
. Interface between KMS,
Traceability system (RFID) and
customers tools (mobile phone,
PDA,…)
(>1 year)
Benefits of MAS for SC
A vision of how information systems will be structured in the
future.
Architecture clearly identifies the differing roles of
function, information and user access
Agents may dynamically respond to change, coordinating
their responses with other agents
Information is distributed to function agents automatically
Information agents manage the evolution of information
Users may tap into other agents, to browse, visualize and
change information, limited by their authority
Conclusion
companies have focused on their core competency and externalized many
sorts of activities.
IT has played a role to this issue in supporting these interactions to
implement relationship strategies. IT permits databases consistency, real time
data exchange and information sharing that are the bases for integrated and
collaborative work
importance of software application integration, such as ERP, as a base for integration
and development of collaborative tools and methods
some industrial cross functional integration through co-managed processes that
integrate both suppliers and customers (VMI and CPFR)
the significant role of product information as a vector of integration all along the
Supply Chain.
Multi-Agent Systems could help in developing future research dealing with these
distributed organizations.
Thank you !
Example MySAP & SAP R/3 ERP
Warehouse Management Systems (WMS)
Advanced WMS
Functionality
Yard
Management
Receiving
Task
Management
Opportunistic
Cross Dock
Putaway
QA
Inventory
Control
Labor
Management
Warehouse
Optimization
Advanced WMS
Functionality
Core WMS Functionality
Cycle Count
Replenishment
Work Order
Management
Pick
Pack
Ship
VAS
Planned
Cross Dock
Reverse
Logistics
Translation and Messaging Bus Backbone
ERP
TMS
OMS
SCP
Material
Handling
Equipment
Transportation Management Systems (TMS)
Order Database
AutoStage
Shipment Consolidation
Planning
Optimized Shipping Plans
Optimized Assignment / EDI
Continuous Moves
Last minute changes / exceptions
In-Transit Visibility
Freight Bill Payment
Operations
Yard Management Systems (YMS)
Trailer Movement Initiated…
– To and From the Dock
– To and From Other Yards
Trailer Movement Completed…
Via the Yard Status View
Via RF