tesi cd - Università degli Studi di Padova

UNIVERSITÀ DEGLI STUDI DI PADOVA
Facoltà di Ingegneria
Corso di Laurea in Ingegneria Gestionale
Tesi di Laurea
Logistica dell'ultimo miglio nelle operazioni in caso di
catastrofe naturale: il caso di Haiti
Relatore:
Ch.ma Prof.ssa Daria Battini
Correlatori:
Ing. Anna Azzi
Prof. Daniel Ekwall
Laureando:
Umberto Peretti
ANNO ACCADEMICO 2010 / 2011
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To you
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Summary
Summary........................................................................................................................... 5
Riassunto ................................................................................................................................... 7
Abstract ..................................................................................................................................... 9
1 Introduction ................................................................................................................. 11
1.1 Description and Motivation .............................................................................................. 11
1.2 Aims ................................................................................................................................... 17
1.3 Overview ........................................................................................................................... 17
2 Humanitarian logistics operations ............................................................................... 19
Literature Review .................................................................................................................... 19
2.1 Logistics History................................................................................................................. 19
2.2 Humanitarian logistics ....................................................................................................... 20
2.3 Humanitarian space .......................................................................................................... 22
2.4 Humanitarian logistics actors ............................................................................................ 24
2.5 Humanitarian logistics issues ............................................................................................ 27
3 Cluster approach .......................................................................................................... 31
Cluster approach ..................................................................................................................... 31
3.1 Cluster approach and coordination................................................................................... 31
3.2 Cluster aims ....................................................................................................................... 35
3.2 The Clusters ....................................................................................................................... 36
3.3 Conclusion ......................................................................................................................... 40
4 Humanitarian lifecycle ................................................................................................ 41
4.1 Humanitarian operations phases ...................................................................................... 41
4.2 Different objectives for different phases .......................................................................... 44
5 Methods ....................................................................................................................... 55
5.1 The phase of the model .................................................................................................... 55
5.2 Data and assumptions of the problem formulation.......................................................... 56
6 Last mile distribution ................................................................................................... 59
6.1 Structure of the relief chain in Last Mile ........................................................................... 59
6.2 Introduction of the problem ............................................................................................. 60
6.3 Literature review ............................................................................................................... 61
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6.4 Model formulation ............................................................................................................ 63
6.5 Mathematical formulation ................................................................................................ 70
7 Haitian case.................................................................................................................. 75
7.1 Infrastructures and damage .............................................................................................. 76
7.2 Port-au-Prince ................................................................................................................... 78
8 Discussion chapter ....................................................................................................... 81
8.1 Demand points in Haiti...................................................................................................... 81
8.2 LDCs location and capacity ................................................................................................ 84
8.3 Distance matrix ................................................................................................................. 85
8.4 Last mile features in Haiti.................................................................................................. 87
8.5 Program proposed............................................................................................................. 88
8.6 Data Input.......................................................................................................................... 89
8.7 Example of Output ............................................................................................................ 92
8.8 Discussion of results ........................................................................................................ 102
9 Conclusions ............................................................................................................... 105
10 Future discussions ................................................................................................... 107
Bibliography ................................................................................................................. 109
Internet sites .................................................................................................................. 113
Attachment 1......................................................................................................................... 115
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Riassunto
Il presente lavoro si propone di affrontare gli aspetti fondamentali della “Logistica
Umanitaria”, da un punto di vista prettamente logistico-gestionale pur addentrandosi in
ambiente filantropico, solitamente non diffusamente trattato in un cursus studiorum
ingegneristico. Tale scelta è motivata, innanzitutto, da una sempre più frequente
incidenza di eventi catastrofici che, anche in virtù della diffusione e accessibilità degli
strumenti mediatici, vengono portati alla ribalta dai mezzi di informazione; in secondo
luogo, tale indagine soddisfa un interesse personale e un’empatia che porta a
condividere e riflettere sui disagi e lo stato di frustrazione che questi accadimenti
comportano.
L’elaborato si articola in quattro parti sinergiche che si sviluppano attraverso un
processo deduttivo. Tali sezioni infatti partono da uno studio generale del settore
umanitario fino ad affrontare nello specifico la criticità della distribuzione degli aiuti
nell’ultimo miglio, ovvero nel segmento più complesso della supply chain, prendendo in
considerazione Port au Prince, capitale di Haiti, come caso di studio per l’applicazione
dei risultati ottenuti.
Nella prima parte dell’analisi, si è ritenuto necessario introdurre la storia della Logistica
e i caratteri dello spazio umanitario; successivamente, si sono approfonditi i ruoli degli
attori che vi operano e i maggiori problemi riscontrabili nell’ambiente in oggetto e non
propriamente usuali nel settore logistico; infine, si delinea il cluster approach, mettendo
in evidenza le modalità di gestione e coordinamento delle Agenzie umanitarie
(governative e non governative).
Nella seconda parte viene illustrato il concetto di ciclo di vita associato ad un disastro
naturale. Attraverso lo studio della letteratura si evince come il presentarsi di nuovi
disastri sia un fatto ciclico e come tale deve essere affrontato. Il più rilevante obiettivo
di questa fase è l’individuazione dei drivers e degli obiettivi caratteristici di ogni fase,
per avere informazioni utili allo studio del modello, in quanto ogni fase presenta
differenti fini e drivers e, di conseguenza, ogni stadio del ciclo di vita deve essere
sviluppato seguendo fattori e programmi specifici. Ciò comporta quindi lo studio di
input, dati, e output, informazioni e livelli di ottimizzazione dissimili.
La terza parte dell’elaborato sviluppa il focus della presente relazione. Si considera,
infatti, la fase denominata First Part of Support, in cui si esaminano il problema della
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distribuzione degli aiuti nell’ultimo miglio e alcune delle caratteristiche del modello che
è stato sviluppato, al fine di chiarire gli strumenti idonei per giungere all’ottimizzazione
della fase del ciclo di vita selezionato. La formulazione matematica del modello è
sviluppata sia dal punto di vista teorico che pratico.
La quarta e ultima parte della tesi è inerente all’applicazione del modello sviluppato. Il
caso di Haiti viene quindi analizzato nella globalità degli aspetti tipici dell’ambiente
umanitario, un settore considerato complesso e di difficile gestione. Il programma,
costruito in matlab, è realizzato sulla base delle particolarità riscontrate sia nella
situazione di Port au Prince (Haiti, 12 gennaio 2010) sia nell’ultimo miglio della
distribuzione a livello teorico.
Nei cenni conclusivi si espongono gli obietti raggiunti secondo i risultati attesi e si
accennano punti di discussione emersi lungo il percorso di analisi.
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Abstract
One of the most critical tasks during the humanitarian operations, after a natural
catastrophe, is to manage and execute all the logistics operations
effectively and
efficiently. The main humanitarian relief operation objective is to deliver the essential
supplies to beneficiaries, people who need daily living supplies into the disaster areas.
The main problems in these logistics operations are the large demands, that is possible
to find after the disaster, and extreme environment. These factors make all the logistics
tasks as a challenge for the logistics operators in the area. This reason, the challenge, is
the main motivation in this research.
The idea is to show what is the humanitarian field and understand the most critical
features, furthermore a model has been used to capture all the main aspects that should
be studied more deeply to have a real operation model, that could be available in the
disaster area.
In the first part of the thesis the main subject is an introduction on humanitarian
logistics operation. In this section humanitarian features are studied, as humanitarian
space, humanitarian logistics actors and issues. Furthermore a feature as cluster
approach is faced in this section with its aims and types of coordination.
The second part introduces the lifecycle. In this section all the phases are faced and they
are studied. The main reason of this part is to understand all the drivers and the
objectives for each phase and choose the most eligible part to develop the model. Indeed
each phase has different objectives and drivers, and so each part should have different
program that can find different optimizations.
The third part of this script is about the Last Mile distribution problem and the model
that has been developed to find the optimization in this kind of situation. All the
features, the data and the assumptions are explained in these chapters. The model
formulation is developed in both theory and practice.
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The fourth and last part of the thesis is about the application of the model. Indeed the
Haitian case is studied with all the characteristics that make the humanitarian
environment so complex. In this case the program, built in Matlab, is made with all the
features both of Haiti and Last Mile distribution problem to find an optimization in the
distribution of humanitarian aids.
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1 Introduction
“A successful humanitarian operation mitigates the urgent needs of a population with a
sustainable reduction of their vulnerability in the shortest amount of time and with the least
amount of resources” (Tomasini and Van Wassenhove, 2004)
1.1 Description and Motivation
Disasters are not evitable. Every time somewhere in the world we can find a lot of
cases of humanitarian disasters. They can be natural disasters (such as earth quake,
famine, tsunami, cyclone, hurricane, flood, etc.), manmade disasters (such as terrorism,
war, civil disorder, etc.), disease (like malaria or hiv/aids) or extreme poverty situation.
Some of them could provide a modicum of warming while others could choke the world
with destruction and chaos.
Almost always the companion of these types of disaster is however the development of
aids, humanitarian aids: materials, equipment, food, funds and people are typical
resources employed to supply relief wherever it needs.
The intention of this project thesis is to understand how humanitarian logistics aid
operations are managed, which performances are studied and which objectives are
important to achieve. In this project we will concentrate in natural disasters effects with
special attention to logistics field, state of humanitarian logistics art, humanitarian
transport optimization problems and their possibilities of resolution.
It is important to pay attention to the choice of research that I have done. I won’t
consider specific manmade disasters but only natural disasters. This choice has been
taken just because manmade disasters, for example wars, could have different issues and
consequences in humanitarian field. Indeed manmade disasters include even more
political and social factors. Just for example, we can take the situation of humanitarian
activities in Serbian-controlled Bosnia on 1993. On 17th of February in that year UN
High Commissioner for Refugees (UNHCR) Sodako Ogata decided suddenly to
suspend al UNCHR activities in Balkan area. This decision was criticized by the
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international community. Former secretary general Boutros Boutros-Ghali criticized her
saying that political negotiations were underway and the suspension of the humanitarian
activities could have political impact. The Sodako’s decision was taken because there
weren’t safe conditions for the humanitarian staff that was working in that area and she
accused local political leaders because they didn’t ensure the safe passage of
humanitarian assistance and blocked access to underserved groups. After few days the
situations changed, safer conditions were created and the humanitarian aid restarted.
However this case is interesting because can show how many political or social
problems it can be found in a war, seen like manmade disaster. Others political or social
example could be taken by others conflicts as civil war in Rwanda.
Indeed, in a conflict, humanitarian organizations can be used like a tool to transform a
conflict or like a tool used for objectives different by humanitarian objectives.
So, to simplify the situation and to concentrate the studies in logistics operations, I have
decided to study just natural disasters.
In particular the intent is to study and understand the Haitian case seeing how the
International community has moved to help this small Caribbean country after the
earthquake that hit Haiti at the beginning of 2010. The choice of this particular event
was made because it was the most important natural disaster in the last ten years and
this can allow to study how the International community has moved to help Haiti.
Based on the United Nations definition, natural catastrophes are classified as great if a
region’s ability to help itself is distinctly overtaxed, making superregional or
international assistance necessary. As a rule, this is the case when there are thousands of
fatalities, hundreds of thousands are left homeless and/or the overall or insured losses
are of exceptional proportions given the economic circumstances of the country
concerned.
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Table 1.1 Great and devastating natural catastrophes in the last fifty years.
Great natural catastrophes since 1960
Deadliest event
Year
Event
Country
Fatalities
2010
Earthquake
Haiti
222.570
2008
Cyclone Nargis
Myanmar
140.000
2008
Earthquake
China
84.000
2005
Earthquake
Pakistan, India
88.000
2004
Earthquake, tsunami
Esp. Indonesia, Sri Lanka, Thailand,
220.000
India
1991
Tropical
cyclone,
storm
Bangladesh
139.000
surge
1990
Earthquake
Iran
40.000
1976
Earthquake
China
242.000
1970
Tropical cyclone, floods
Bangladesh
300.000
1970
Earthquake
Peru
67.000
This would be a classic logistics case. The main differences between this case and the
others are about the particular situation in which the humanitarian logistics aid operators
have to work: complex environment with uncertainty and risk following humanitarian
principles. However humanitarian aid operations camp is a particular field, full of
difficulties, in which we can find logistics operations. Indeed the humanitarian operators
have often to work in an environment with destabilized infrastructures ranging from a
lack of electricity supplies to limited transport infrastructure. But even it can be an
interesting field of study, indeed in this humanitarian branch a quick response to the
disaster is fundamental together with a resource management able to give humanitarian
relief in time and with a cost efficiency. The speed of humanitarian aid operations after
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a disaster depends “on the ability of logisticians to procure, transport and receive
supplies at the site of a humanitarian relief effort” (Thomas, 2003). So it is a logistics
case in disaster relief operations with very special circumstances.
I have chosen this particular field even for another reason: the natural disasters with all
types of aid they need, have been increased and now are still increasing. Indeed,
according to Thomas and Kopczac, (2005) “disaster relief is and will continue to be a
growth market. Both natural and manmade disasters are expected to increase another
five-fold over the next fifty years”.
There are a lot of factors that are present and are increasing day by day. For example
there are factors as steady population growth, urbanization and residential densification.
Furthermore there are some natural factors, as desertification, and economical factors,
as natural resources research. All these three types of factors can explain why it has
been predicted this increasing of disaster relief market by Thomas and Kopczac.
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Figure 1.1 Natural disasters reported: in the last thirty years there has been
averagely an 900% increase.
According to Munich Reinsurance Group and CaritasItaliana studies, the natural
disasters with all types of aid they need, have been increased and now are still
increasing.
Since 60’s until today, the victims number of natural disasters have had averagely a
900% increase, more or less the same increase of natural disasters. This is caused
mainly by the living conditions in poverty of half world population: uncontrolled
population growth, forced urbanization, abandonment of countryside, lack of
infrastructures and public services, poor quality of building, poor land management,
social degradation as well as the overlapping of wars and environmental disasters.
These catastrophic events hurt differently rich and poor countries. Indeed if a region is
rich, it will have more economic losses, while if a country is poor, it will have more
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casualties. And so it is possible seeing the table 1.1 understand why in the list of 10
most important natural disasters in terms of fatalities, we can’t find any rich states.
In a Munich Reinsurance Group’s study we can find a statistics about the real annual
economic losses caused by natural disasters. Is it possible to see that annual economic
losses have been growing steadily. Averaging Us$75.5 billion in the 1960’s, US$138,4
billion in the 1970’s, US$213,9 billion in 1980’s and US$659,9 billion in 1990’s.
Furthermore is important to observe that Logistics has always been an important factor
in humanitarian aid operations, according to Trunick in 2005 logistics efforts account
for 80 percent of disaster relief.
Transportation is a major component of disaster relief operations. One of the most
important challenge is the post disaster transportation, especially across the “last mile”.
If we take a typical supply chain and if we consider it in a natural disaster situation, it is
easy to understand that the last part of distribution is the most critical one. There are a
lot of factors that make this part so critical. The most important factor is the number of
the people, that usually is uncertain, and the issue that these people need a quick
response.
Figure 1.2 : The main element of a typical supply chain. In relief operations
ultimate costumers management is the most critical part (Ittmann, 2009).
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Furthermore “the challenge arises from damaged infrastructure, limited transportation
resources, and the sheer amount sand bulk of supplies to be transported” (Balcik et al.,
2008). Even the geographical characteristics of a place may also increase challenges in
accessing affected populations (e.g., the geographically dispersed Indonesian islands
and the mountainous terrain of Pakistan). These characteristics may imply the use of
already-scarce resources.
Therefore, for all these reasons, it is possible define the logistics as critical point in
humanitarian aid operations.
1.2 Aims
This thesis aims to present the most basic concepts of humanitarian logistics: the state
of art upon humanitarian logistics and last mile distribution in a humanitarian aids
operation, taking Haitian case as example. The logistics principles that will be described
here have multi-sectoral applications, not only in emergency situations, but also in the
day-to-day operations that must be a part of disaster prevention and preparedness.
1.3 Overview
In chapter 2 the humanitarian logistics is introduced. Indeed in this section it is possible
to find the history of commercial logistics and the humanitarian one. Furthermore some
particular features of the humanitarian logistics are explained as actors, space and issues
typical of this kind of field. The idea of this chapter is to introduce the topic of the
humanitarian giving the basis for the next sections.
Chapter 3 is a prosecution of number 2. In this section the cluster approach has been
studied. This approach was introduced in 2005 and it is known as the humanitarian
reform that changed completely the humanitarian field, indeed it brought a new concept
of coordination and, with it, a higher level of efficiency. So in the chapter are faced the
aims of this reform and all the clusters presented in the humanitarian field.
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In chapter 4 the lifecycle of a disaster is considered. In this chapter the different phases
of a disaster are explained. The main reason of this choice is to show all the objectives
and drivers that change in each phase. This chapter allows to understand which can be
the future drivers that will be used in the next chapters.
In chapter 5 it has been taken the phase where the Last Mile problem will be put. The
phase chosen is the First part of the Support phase. Here all the drivers have been found
and the data and the assumptions of the problem are faced.
In chapter 6 the Last Mile distribution problem is studied. I here we can find the
literature review and the structure of the relief chain in this particular problem.
Furthermore the model formulation is presented with both of the phases used. At the
end of the chapter it is possible to find the mathematical formulation of the problem,
with the objective function and all the constraints, and a table shows the differences
between our model and the model studied previously.
In chapter 7 there is the Haitian case presentation. This chapter would start the real
application of the model. Indeed the case is studied to see if it is possible to find all the
assumptions and the features studied in the previous chapters. Here it is even found Port
au Prince as the place where the study will be placed, furthermore some of the data are
presented.
In Chapter 8, Haitian case study is conducted to apply model in a humanitarian logistics
operation. The model is used in this study to generate an optimization in the
distribution. In this case studied the model is executed based on some inputs to find the
most critical data that are required for this situation. Finally, the chapter summarizes
both theoretical and computational results obtained by the study.
In chapter 9 all the conclusions are presented and discussed.
In addition, in chapter 10, future research directions are provided.
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2 Humanitarian logistics operations
“Logistics in the Humanitarian sector is define as ‘the process of planning, implementing and
controlling the efficient, cost-effective flow of and storage of goods and materials as well as
related information, from point of origin to point of consumption for the purpose of meeting
the end beneficiary’s requirements” (Thomas and Mizushima, 2005)
Literature Review
The field of humanitarian logistics is quite new and it is usually considered as the
process and system involved in providing humanitarian aids to help vulnerable people.
The logistics is very important in humanitarian operations, indeed it efforts account for
80 percent of disaster relief.
2.1 Logistics History
It is possible to find the origins of logistics already in antiquity and they had a military
connotation. Indeed logistics was considered like a branch of military art that dealt all
the tasks that military needed living, moving and fighting in the best conditions. This
logistics identification, like an exclusively military task, remained valid till the end of
second world war. Just after the end of second world war the concept of logistics started
to be expanded and it was extended even to economic and industrial sector. In 50’s and
60’s the meaning of logistics was limited to finished product distribution, it was
confined like a support task. In the 70’s it is possible to find the first forms of evolution
of logistics concept. It started to change from a product distribution to a structured
management of a whole of activities. The enterprises began to look for improvements in
physical distribution area, from warehouses to customers. It was made through
appropriate interventions of rationalizations hat were used to optimize different parts of
distribution cycle. Since 80’s, after new logics of management in the enterprises were
introduced, for example Materials Requirements Planning (MRP) or the Just in time
(JIT), the attention was focused upon material management. Indeed "material
management" was coined to mean all those activities used to provide the correct
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acquisition, handling and management of materials to ensure the constant and sudden
supply to production and to others users.
The next phase in logistics evolution marked a radical change. This happened because
there was the transition from a logistics considered as a whole of operative activities to
a logistics seen as inter-operational system that was used in achievement of higher
performance levels. In that period started the concept of integrated logistics, well
synthesized in the definition made by the Council of Logistics Management in 1986. In
that definition the logistics was seen as the process used to plan, implement and check
the prime material flow, semi processes and finished product flow, and to manage their
information flows, from the origin place to the customer. This was done to find of
making the product as efficient as possible and as comply as possible to costumers
needs.
The last stage of the logistics evolution, the stage that leaded to the born of Supply
chain management concept, was characterized by awareness that the improvement of
flows management in logistics chain has to be done with the involvement of external
actors. The logistics assumed a central role and its objective became substantially
governing all the phases of the productive process, even external from the enterprise. It
is seen as a new management approach, in which the enterprise has became a part of a
network and in this network the different organizations integrate their business
processes to supply products, services and information that can create value for the
customers.
2.2 Humanitarian logistics
The humanitarian logistics had followed the history of logistics but just since 60’s and
70’s.
The conflict in Biafra is one example of the first situations where issues of more
contemporary complex emergencies began to develop .
In 1968 in Biafra, a Nigerian region rich of oil, an entire generation of children was
starving. This happened because there was a war between Nigeria and Biafra, that
wanted to be independent. In that period a thousand children were dying every day for
the famine that had touched the country.
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When an English journalist went there with a photo reporter to make pictures about the
situation to show it at “western world” the story of Biafra became soon one of the most
emblematic affair in those years. Everyone was affected by that tale. And, after a lot of
events meetings and protests, humanitarian organizations were born as we now know
them now. Many humanitarian organizations grew thanks to their Biafra’s employment.
Organizations like International Red Cross tripled its investments, even others nongovernmental organizations as Oxfam, Caritas or Concern grew.
That was an heroic behavior and a logistics success for humanitarian organizations.
Ideas and underlines principles weren’t new, but from Biafra these took shape and
spread with strength.
Indeed before this we could just speak about logistics as military or industrial branch.
For this reason it is considered relatively new, a field that has been began relevant
research just within last ten years.
According to Thomas and Mizushima definition the Humanitarian logistics is
considered the process of planning, implementing and controlling the efficient, costeffective flow of and storage of goods and materials as well as related information, from
point of origin to point of consumption for the purpose of meeting the end beneficiary’s
requirements. It is different to commercial logistics because there are a lot of issues and
actors that there aren’t in commercial one. In the next paragraphs actors and issues will
be exposed. Furthermore humanitarian space, which is defined by humanitarian
principles, both physically and virtually, where humanitarian operators need to be able
to operate, will be developed in next sections.
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2.3 Humanitarian space
Figure 2.1 : Humanitarian space, defined the space between the three main
humanitarian principles: humanity, neutrality and impartiality.
In the mid-1990s the term “humanitarian space” was coined by Rony Brauman former
Médecins Sans Frontières (MSF) president who defined it ‘a space of freedom in which
we are free to evaluate needs, free to monitor the distribution and use of relief goods,
and free to have a dialogue with the people’.
This topic was resumed by Tomasini and Van Wassenhove in 2004. They have
considered the humanitarian space as an area defined by three main important
humanitarian principles: Humanity, Neutrality and Impartiality. In the humanity
principle, the purpose is to protect health and life ensuring respect for human beings
wherever they are found. Neutrality is a principle about humanitarian operators’
neutrality by political, racial, religious or ideological situations. For this reason
humanitarian operators must not take sides in hostilities or engage in controversies. The
last principle is impartiality. It means humanitarian operators don’t make any distinction
on the basis of nationality, race gender, religious belief, class or political opinions.
For Tomasini and Van Wassenhove humanitarian space is a space both virtually and
physically. Indeed, virtually, the humanitarian space is useful to help humanitarian
operators’ decisions to ensure their bearing with ethical context. Physically, instead, the
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space is described as a “zone of tranquility” where it is possible giving humanitarian
aids to people who need, in a context of safety and operational freedom.
Clinging to humanitarians principles and having the right humanitarian space in
complex environments could be very difficult, particularly during a war. Indeed
humanitarians have to remain independent by the conflict, for example they can’t use
humanitarian aids to help one side over another, while they have to be able to work in
safety. Historically is quite easy to find situations in which humanitarian space were
important. For example in Iraq and Afghanistan situations the separation between
humanitarians and military has been sometimes blurred. In these situations it happened
sometimes that humanitarian community was confused with combatant military force
and so the humanitarian space, seen like “zone of tranquility”, was not available.
It is possible even to study “The South African food crisis in 2002” to understand the
importance in adherence to humanitarian principles in crisis due by natural disasters and
the challenge that humanitarian operators have to take to uphold and maintain their
principles and “space”.
In February 2002 was declared a state of emergency in Malawi, an African nation. Two
months later even Lesotho and Zimbabwe had declared the state of emergency. In this
crisis there were many factors involved, that made the crisis more complex. Indeed
political, economical, demographic and environmental factors were preset in this crisis.
The WFP started to move to South African crisis quickly with food donated by US
Government. But there was a problem with US donations, indeed US, like many others
donors, made no distinction between genetically modified (GM) and conventional food.
So many African countries refused the food because they were afraid of contamination
and because their economy could be damaged by GM in long-term. The WFP decided
to change his programs to follow its principles. It respected the decision even if it could
be a big problem. Indeed WFP supply chain was still working with a lot of genetically
modified food and its warehouses were full of it. The organization was able to move
quickly following humanitarian principles, even it was able to turn into a positive
situation from negative one.
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2.4 Humanitarian logistics actors
In humanitarian aid operations it is possible to find many actors that are employing.
According with Gyöngyi Kovács and Karen Spens as well as population hit by natural
disaster, that is the last beneficiary, there are others actors in humanitarian aid supply
network. These are logistics providers, military, governments, donors, aid agencies and
the others NGOs.
Figure 2.2 : Actors in supply network in humanitarian aid operations.
Source: Kovács & Spens, 2007
Logistics providers: they are presented in the field to ensure the providing of the
humanitarian aids.
•
Aid agencies and others NGOs: aid agencies can be considered as the first actors
involved by governments to alleviate suffering while NGOs are not linked to
any government. Some of them, the largest ones, could be considered as global
actors but there are even many smaller agencies, regional or country-specific
agencies. The high number of humanitarian agencies has made important
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improving coordination between them, for this reason in 2005 was introduced
the Custer approach that will be studied in the next phases.
Table 2.1 : Annual budgets of major humanitarian NGOs and agencies.
NGO/agency
Annual budget
($US billion)
UN Childrens’ Fund (UNICEF)
3,390
World Food Programme (WFP)
5,000
UN High Commission for Refugees (UNHCR)
1,095
World Health Organisation (WHO)
4,225
UN Development Fund (UNDP)
5,000
UN Population Fund (UNPF)
250
(UN) Office for the Coordination of Humanitarian Affairs (OCHA)
240
World Vision International (VWI)
1,620
Save the Children
810
CARE
440
Catholic Relief Services
440
Me´decins sans Frontie`res (MSF)
430
Oxfam
400
International Federation of Red Cross and Red Crescent Societies (IFRC)
500
Total
23,840
•
Donors: donors are the base of humanitarian relief and so they have to be
considered very important actors. Indeed they provide most founding for relief
operations. Recently private donors and foundations, in addition to government
specific founds, have become important sources of funds for humanitarian
agencies. It is important to know that it is important to coordinate even the
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donors, indeed they usually are a large number of uncoordinated and disparate
donors and they could be influenced by media.
•
Military: military often can be very important actors. When aid operators need
personnel to provide assistance. For example many roads (including the airport
road) in Port au Prince in Haiti in 2010 were being cleared by MINUSTAH
(United Nation Stabilization Mission in Haiti) and Brazilian battalion. It is to
underline that military mission are usually very fast in terms of reaction time and
it is very important especially at the beginning response to a natural disaster.
•
Governments: both host governments and neighboring country governments are
important actors during natural disaster response. Indeed host governments
usually control assets such fuel depots or warehouses and have information
about the country and the population. Furthermore host country logistics service
providers can facilitate or constrain the effectiveness of relief operations.
Neighboring country governments are important instead because they can
provide logistics support and quick response to natural disaster. For example, in
Haiti case, one of the first response was made by Cuba while logistics support
was given and is given still now by Dominican republic.
•
Logistics providers: in the supply process can be very important even extraregional logistics service providers. An example could be DHL’s participation in
Humanitarian logistics operations in Haiti. Indeed with so many shortages,
security and logistical challenges in Haiti, the DHL’s skills at managing aid
were absolutely necessary.
•
The media: this actor usually is not presented in private logistics sector and it
could be a very critical which can change donors’ appeal to humanitarian
operations. Sometime humanitarian organizations have problems in managing
“unsolicited donations” due advertising made by media and that could cause
bottlenecks in the supply chain.
26
With all these actors one of the most important challenges is the coordination. Indeed, if
it is not present a good coordination could be impossible providing humanitarian aids
with good efficiency. For this reason in 2005 cluster approach was accepted by Interagency Standing Committee (IASC). This approach is fundamental in humanitarian aids
operations indeed clusters on different functions, including sheltering and logistics and
water sanitation, can be viewed as an effort of functional coordination. Cluster approach
will be studied in next chapter.
2.5 Humanitarian logistics issues
Humanitarian logistics is very different from typical commercial logistics. As we have
seen in humanitarian field there are a lot of actors that usually there aren’t in
commercial one. Furthermore it is possible to find others issues that make relief
distribution more complex. These issues are about environment, uncertainty and
resources in which humanitarian logistics aids operations often take place.
According to Tomasini and Van Wassenhove it is possible identify some characteristics
of humanitarian supply chain that can explain its complexity:
•
Ambiguity of objectives. The first characteristic is the ambiguity of objectives, it
is due to large numbers of stakeholders. Indeed, as we have seen, in
humanitarian operations there are many actors and sometimes their actions can
be “uncoordinated, spontaneous, unsolicited, and disparate” (Rolando Tomasini
And Luk Van Wassenhove, 2010). So the absence of a profit-making incentive
and the absence of a coordination could lead to continuous reinvention of the
route.
•
Limited resources. Another characteristic that has been studied is the limited
resources mixed with asymmetric distribution in the investments of the different
actors. The limited resources studied are human, capital and infrastructures’.
Human resources are limited because it is difficult to find qualified people
deployable in so difficult contests, even money liquidity can’t be always
27
available in time. The infrastructures, instead, in a natural disaster situation can
be damaged and so unusable.
•
High uncertainty. In a post natural disaster scenario it is possible to find a very
dynamic situation with many changes in supply and demand. Furthermore there
is always a significant inability assess the situations. This happen for both
presence of many actors and the difficulty to understand how they will
contribute to the operations. This uncertainty is also linked with an high level of
risk indeed in humanitarian operations it is required to deal with unknown and
unexpected events.
•
Urgency. Humanitarian operations usually have an high level of urgency. Indeed
it is important that first humanitarian aids arrive in less than 72 hours. This
brings these types of operations in a high level of intensity (seen as number of
task in a time unit).
•
Environment. As we have seen, in humanitarian operations there are a lot of
actors and sometimes could be difficult maintain “humanitarian space” where
humanitarians can supply aids without any pressure and independently. When
we consider post natural disaster environment, we have to consider even a
context between life and death with an high level of physical and emotional
stress. At last humanitarian are forced by the particular environment in using
robust equipment, for example all terrain trucks, this type of environments are
typical after natural disasters where there are high structural damages (streets,
buildings).
All these issues are typical of humanitarian supply chain but if we consider logistics
issues we can find another one important, that is the “speed”. In industrial logistics
often the speed of transport is fundamental but in humanitarian field the speed of
response absolutely critical. This happens because humanitarian operators have 72
hours to save a maximum of human lives after a natural disaster. So the speed is the
main diver and the reduction of lead time is one of the most important challenges.
In 1994 Richardson elaborated a list of factors that could increase the complexity of the
humanitarian operations and has to be considered when we are speaking about a
28
disaster. These factors were not real factors themselves but they can be included in
complex management of a disaster.
These elements are:
•
Interactivity: it is possible interactivity among factors, this could worsen the
situation and escalate the magnitude of disaster.
•
Invisibility: some factors could be unknown or they are underestimated, this
leads to an inability to anticipate these factors.
•
Ambiguity: when the cause-effects are not completely known it could be
difficult to understand the direction in which magnitude might escalate.
•
Incremental: sometimes some factors could be disregarded because they are
considered not so important, becoming invisible and growing, leading to future
consequences.
•
Diversity of factors: in humanitarian aids operations it is normal to work with a
lot of factors and it could be difficult to understand which are the most
important ones and if some of them could hide the real nature of the problem.
•
New phenomena: one of the most challenge in an after natural disaster
operation is that often effects and impact could be unknown and without time
for an appropriate analysis of the situation.
As we have seen there are a lot of challenges in humanitarian aids operations and also
there are many factors that could increase the complexity in this type of operations, for
these reasons it is important to have a good supply chain management from suppliers
until ultimate customers, with high attention especially upon problem in last mile
distribution.
29
30
3 Cluster approach
“Humanitarian reform seeks to improve the effectiveness of humanitarian response by ensuring
greater predictability, accountability and partnership. It is an ambitious effort by the
international humanitarian community to reach more beneficiaries, with more comprehensive
needs-based relief and protection, in a more effective and timely manner.” (United Nations
website)
Cluster approach
According to van Wassenhove (2006) the increasing number and complexity of
disasters has made specialization and coordination both important and challenging. As
we have seen there are many actors in humanitarian aids operations, organizations that
provide humanitarian aids, both in immediate response to a disaster or in the months
that follows. They have their independence. When these organizations combine each
others can be rise a series of problems related to coordination, to transport optimization
and to prioritizing the pipeline. For these reasons this kind of operations need
“coordination in terms of both preparedness and response, such as contingency
planning, needs assessment, appeals, transport management and last-mile distribution”
(Oloruntoba, 2005).
One attempt to solve these problems is the cluster system, essentially a template for how
coordination should be carried out in a number of areas (Jahre and Jensen, 2010).
3.1 Cluster approach and coordination
According to Inter Agency Standing Committee the cluster concept could be defined
functionally in terms of areas of activity – for example, water and sanitation, health,
shelter and nutrition – which typically reflects the important and somewhat separate
areas of relief work, often referred to as sectors (Inter-agency Standing Committee
(IASC), 2006).
In this research we study the logistics cluster and we can say that it is relatively well
developed. This because it has the experiences of the United Nations Joint Logistics
Centre (UNJLC), a mechanism uses previously for the logistics coordination. The
31
UNJLC was institutionalized in 2002 by the UN and it was employed just to coordinate
humanitarian logistics in humanitarian aids operations. Since 2005 it was gradually
superseded by the cluster system. The logistics cluster has a very important task because
it supports other clusters, for example in distribution or in transportation.
Cluster approach was introduced in 2005 because it was obvious that, for example in the
Asian tsunami in December of 2004 and the response to the Darfur crisis in 2004/2005,
there were a lot of problems providing sufficient coverage in large aids operations. For
example, if we study transport optimization in a humanitarian aids operation and we
consider all of the actors employee in the operation it is easy to understand that without
any coordination we could have different transports for each organizations employed. In
this way we can find transportations far from full load or from route optimization.
Table 3.1: Weaknesses and threats in humanitarian operations before the
Humanitarian Reform in 2005 (Jahre and Jensen, 2010).
So one of the most important concepts introduced by cluster approach was the concept
of coordination. There are a lot of different typologies of coordination that have been
suggested, but most of them are in relation to vertical and horizontal coordination, as in
Figure 3.1.
32
Figure 3.1: Different typologies of coordination that have been suggested, in
relation to vertical and horizontal coordination
A good definition of coordination could be “when two or more unrelated or competing
organizations cooperate to share their private information or resources such as joint
distribution centers” (Simatupang and Sridharan, 2002)
As is it possible to see in the table 3.2, take by Coordination in humanitarian logistics
through clusters (Jahre and Jensen, 2010), there are different reasons to consider each
type of coordination, horizontal or vertical. The first reasons are about what to
coordinate and about the focus of each type of coordination, while the second point
touches the objectives that we want to achieve if we choose one of them.
33
Table 3.2 Coordination aspects in Horizontal and Vertical coordination (Jahre and
Jensen, 2010).
In the literature it is possible to find description about vertical coordination. It is most
used to synchronize the supply chain at different levels and its objectives are to improve
the efficiency of the whole supply chain and focusing all the suppliers to the final
costumers improving costumers service while horizontal cooperation is defined as
“concerted practices between companies operating at the same level(s) in the market”
(Cruijssen et al., 2007) and it involves internal collaboration and collaboration with
competitors and non-competitors (Barratt, 2004).
The importance of coordination in humanitarian field could be understood by keeping in
mind that a humanitarian operations is not subjected just by UN and its agencies, but
there are a lot of workers and NGO organizations that could be employed in this kind of
operations and that often lack the political support and recognition of the UN system.
Thus in humanitarian logistics is very important to consider even the horizontal
coordination. Indeed in the cluster concept, the main task of the cluster leader is to
create global standards and work with other agencies, seen as external collaborators in
the figure 3.1, to secure capacity such as human resources, there are also some examples
of pipeline management, such as the logistics cluster handling air traffic control and
cargo prioritization (Jahre and Jensen, 2010). Furthermore, when UN humanitarian
system was projected, years ago, it was divided into specialized tasks (UNICEF for
children, WFP for food, etc.) but we know that no crisis is ever just about one of the
tasks, usually many of them are employed in the same operation.
34
For these reasons the cluster approach is used from the beginning to plan and organize
and coordinate the international response in emergency that requires a multi-sectoral
response and that considers the participation of a wide range of international
humanitarian actors.
3.2 Cluster aims
To understand completely the cluster approach could be interesting understanding
cluster aims. The cluster concept was developed to improve efficiency in 5 key areas
(Cluster Appeal 2007):
1) Sufficient global capacity to meet current and future emergencies
2) Predictable leadership at a global and local level
3) Strengthened partnerships between UN bodies, NGOs and local authorities
4) Accountability, both for the response and vis-à-vis beneficiaries
5) Strategic field level coordination and prioritization.
Below we are going to tackle each key areas and to present the particularity of them to
understand how the cluster approach can be useful in humanitarian aids operations:
1. The first aim of the approach is to ensure sufficient global capacity. It is possible
obtain it building up and maintained the capacity in all the main sectors of
response. This has to be done ensuring timely and effective responses in new
crises.
2. Second aim of the cluster approach is to ensures predictable leadership in the
humanitarian operation, in each areas of response. For this reason each cluster
has a cluster leader that is responsible for ensuring that the capacity is in place
and that the others activities , as assessment, planning and response, are carried
out in accordance with partners and following agreed standards and guidelines.
3. As the third aim, the approach is planned around the concept of partnerships
between the agencies: UN agencies, the International Red Cross and Red
Crescent Movement, international organizations and NGOs. They work together
towards agreed common humanitarian objectives. These objectives are both at
the global level (preparedness, standards, tools and capacity-building) and at the
35
field level (assessment, planning, delivery and monitoring during the operation).
The aim is even to make a better partnership with host governments, local
authorities and local civil society, and to avoid situations were governments
have to deal with hundreds of uncoordinated international actors.
4. Fourth aim, the cluster approach strengthens responsibility. Indeed cluster
leaders are accountable to the Emergency Relief Coordinator (ERC) for building
up a more predictable and effective response capacity in line with IASC
agreements. At the field level cluster leaders are accountable to Humanitarian
Coordinators for fulfilling agreed roles and responsibilities for Cluster
leadership. The second important point for the responsibility is that cluster
approach strengthens accountability to beneficiaries through commitments to
participatory and community-based approaches, improved common needs
assessments and prioritization, and better monitoring and evaluation.
5. The fifth and last aim considered is that the approach should help to improve
strategic field-level coordination and prioritization in specific areas of response
by placing responsibility for leadership and coordination of these issues with the
competent operational agency.
(These information are tracked and developed by Humanitarianreform.org)
For these five reasons the cluster approach is very important in management of
humanitarian aids operations.
3.2 The Clusters
As we have seen the cluster approach, since humanitarian reform in 2005, is the answer
that has been given to resolve some problems in humanitarian operations. The clusters
present now, in 2010, are eleven: Agriculture, Camp Coordination/Management, Early
recovery, Education, Emergency Shelter, Food, Emergency Telecommunications,
Health, Logistics, Nutrition, Protection, Water Sanitation Hygiene.
In the next section we are going to expose each of them.
36
Table 3.3: Clusters and cluster leaders that could be involved in a humanitarian
operations
Cluster
Agriculture
Camp Coordination/Management
Early recovery
Cluster leader
FAO
UNHCR and IOM
UNDP
Education
UNICEF and NGO
Emergency Shelter
UNHCR and IFRC
Emergency Telecommunications
WFP
Food
WFP
Health
WHO
Logistics
WFP
Nutrition
UNICEF
Protection
UNHCR
Water Sanitation Hygiene
UNICEF
Agriculture Cluster: in agriculture cluster the leader is the Food and Agriculture
Organization (FAO) of the United Nations. It provides services as technical surge
capacity, improved sector response, agreement on common methods and formats for
needs assessment, monitoring and benchmarking, opportunities for best practices and
lessons learned, and mobilization of resources for both the sector as a whole and FAO’s
own interventions.
Camp Coordination/Management Cluster: IOM (International Organization of
Migration) and UNHCR ( United Nations High Commissioner for Refugees) are the
cluster leaders of the global CCCM Cluster. This cluster has an unified approach for
both natural disasters and manmade disasters. This is made with the purpose of ensuring
commonly agreed policies, standards and expertise are applied to IDP (internally
displaced person) situations regardless of the source of displacement (natural or
manmade). Anyway the agencies maintain their primary responsibility in their field:
with UNHCR addressing conflict-induced IDPs and with IOM addressing natural
disaster-induced IDPs.
37
Early Recovery Cluster: in this cluster the leader is UNDP (United Nations
Development Programme). The cluster is defined as recovery that begins early in a
humanitarian setting. It is a multi-dimensional process, guided by development
principles, it aims to generate self-sustaining nationally owned and resilient processes
for post-crisis recovery, the last part of the lifecycle. Early recovery encompasses the
restoration of basic services, livelihoods, shelter, governance, security and the rule of
law, environment and social dimensions, including the reintegration of displaced
populations. It stabilizes human security and addresses underlying risks that contributed
to the crisis.
Education Cluster: UNICEF (United Nations Children’s Fund) and the International
Save the Children Alliance co-lead the IASC Education Cluster, working in close
collaboration with other leading agencies and INEE (Inter-Agency Network for
Education in Emergencies). It is increasingly recognized as an important sector within
humanitarian response.
Emergency Shelter Cluster: this cluster is co-chaired by UNHCR and IFRC
(International Federation of Red Cross and Red Crescent Societies). UNHCR leads the
Emergency Shelter Cluster in the area of conflict generated IDPs while IFRC is
convener of the Emergency Shelter Cluster in natural disaster situations.
Emergency Telecommunications Cluster: In 2010 WFP was designated as the Global
ETC Lead Agency with responsibility as the provider of last resort (POLR) for security
and data communications. The availability of robust, reliable information and
communications technology (ICT) infrastructure and services has become critically
important to the successful functioning of all the clusters and for ensuring personal
security from the onset of an emergency.
Food cluster: the leader in this cluster is the WFP and its function is to deliver food
where people need during a humanitarian operations. Important to note how WFP is the
38
leader even of Logistics cluster, this because distribution of food and logistics are
closely linked.
Global Health Cluster: it is under the leadership of the WHO (World Health
Organization), is made up of more than 30 international humanitarian health
organizations that have been working together over the past four years to build
partnerships and mutual understanding and to develop common approaches to
humanitarian health action.
Global Logistics Cluster Support Cell: is hosted in the Logistics Division of WFP. Its
primary goal is to mobilize surge capacity to provide logistics support to the
humanitarian community in emergencies and to work with local authorities on systemwide preparedness and contingency planning.
Nutrition Cluster: the leader of this cluster is UNICEF. It ensures the right information
gets to the right people in time and accessible manner. The four focus areas for the
Nutrition Cluster are strategic ones and they are not meant to be exhaustive and they
include coordination, capacity building, emergency preparedness, assessment,
monitoring, surveillance, and supply.
Global Protection Cluster: the global lead agency for protection is UNHCR. Protection
covers a wide range of activities that are aimed at ensuring respect for the rights of all
individuals, regardless of their age, gender or social, ethnic, national, religious or other
background.
Water Sanitation and Hygiene (WASH) Cluster: it provides an open formal platform for
all emergency WASH actors to work together. The global cluster is UNICEF. The
Cluster Approach presents many opportunities to bring the sector as a whole closer
together in ensuring a predictable, effective, timely and coherent WASH humanitarian
response.
(These information are tracked and developed by www.oneresponse.info)
39
3.3 Conclusion
Figure 3.2 : The coordination in cluster approach, from the high number of Aid
Agencies and NGOs to the disaster situation.
In 1998, the term “cluster” was defined by Porter as “geographic concentrations of
interconnected companies and institutions in a particular field”. In the field of
humanitarianism, cluster thinking has been suggested as a solution to the lack of
coordinated disaster response. Clusters for diverse functions, including sheltering,
logistics and water and sanitation, can be viewed as an effort to achieve functional
coordination (Jahre and Jensen, 2010).
The aim of this chapter was to show and understand the cluster approach and its impact
in humanitarian aids operation. As it is possible to see in figure 3.2, the clusters are used
to manage, coordinate and optimize all the resources (human resources, physical
resources, information, etc.) from the high number of aid agencies and nongovernmental organizations to the place where the humanitarian operation is placed, the
disaster situation.
40
4 Humanitarian lifecycle
“Disaster management is often described as a process with several stages (Long, 1997; Nisha
de Silva, 2001). Cottrill (2002), borrowing from the risk management literature, talks about the
planning, mitigation, detection, response and recovery phases of disaster management.”
(Kovàcs and Spens, 2007)
An humanitarian operation has not to be considered just one and whole operation.
Indeed it’s better considering it like a set of phases, this is important because the
amount and the types of resources and the supply chain are usually different on each
phase of operation. These phases constitute jointly the lifecycle.
Figure 4.1 : Life span of a disaster.
4.1 Humanitarian operations phases
Before studying each phases it is important consider the two types of project
environment for implementing humanitarian logistics operations (Charles, Lauras,
Dupont, Tomasini and Van Wassenhove, 2007).
The first type of disasters are slow onset disaster, as epidemics, famine/food insecurity,
population movements, here the humanitarian operations have constraints as cost
savings, low budgets and the focus is based on the capacity building, on using national
staff, on planning and scheduling, and long time frames. The second type of disasters is
41
sudden onset disasters, for example hurricanes, cyclones and typhoons, earthquakes,
floods, volcanic eruptions. Here there aren’t close constraints as budget but there are
short time frames, and they are centered on providing medical assistance, providing
food and non food items.
This distinction is important to understand the different the supply chain and the amount
of resources needed for each type of disaster but especially to understand the different
space-time for the different types of disaster.
The response typically follows the life cycle represented in figure 4.1. The response can
be divided in three principal phases:
•
Phase 1, ramp up, immediate response: in this phase aids and infrastructures
(assets and staff) are deployed to the area of disaster.
•
Phase 2, sustainment, support: here aids and infrastructures are employed
completely all period of responding to the crisis long.
•
Phase 3, ramp down, dismantling: during the dismantling assets are gradually
reduced, the assets are moved to others areas where they are needed.
Later, in 2007, was presented by Kovács and Spens a model that considered three
phases of disaster management. The names of each phases were preparation, immediate
response and reconstruction. In this model there was an important additional phase: the
preparedness.
Figure 4.2 : Phases of disaster relief operations. (Kovács and Spens, 2007)
However if we combine the two models studied, Tomasini and Van Wassenhove’s
model with Kovács and Spens’ model, it is possible to arrive to the model presented by
Charles et al. in 2007.
42
Figure 4.3 : Humanitarian operations lifecycle with the time-spaces for the two
different types of disaster. (Charles et al., 2007)
In the figure 4.3, furthermore, it is possible to note the timescale of four phases in the
sudden onset and slow onset disasters, but it is important to understand that it is just an
estimate indeed it is not always possible to be absolute in terms of the timescale of these
four elements. A broad estimate was made by Tatham and Kovács (2007) while in the
example in figure 4.3 we can see the time-space made by Charles et al. (2007), in which
they suggest that, in a sudden onset disaster, the immediate response element can last 46 days after the onset of the event while the support element can last from three months
to one year. In a slow onset disaster the time of each element is more extended. This
study is important to note that in the immediate response phase in a sudden onset
disaster, the reliefs are provided by local and national resources, while the second phase
there is, generally, support from international agencies. In essence, the differentiation
between the two phases reflects the reality that humanitarian operators have more or
less 72 hours to react and respond to a disaster.
A final consideration about lifecycle is about the cyclical nature of disaster relief. As
presented by Safran (2005) and Houghton (2006). Here the reconstruction phase of
disaster reliefs is seen as an essential link to a new preparation phase for future
disasters. This view is very precious, indeed if we consider the case of Haiti we can
43
understand how the reconstruction phase after 2005 hurricane Wilma in Haiti could be
considered as the phase before of preparedness phase for 2010 earthquake in Haiti.
4.2 Different objectives for different phases
As we have seen in section 4.1 there are more phases in the lifecycle of a disaster and
each of these phases have different objectives and drivers during humanitarian aids
operations. In this paragraph the aim is to understand and explain each phases and its
objective and drivers.
Studying the literature about humanitarian operations, in particularly humanitarian
logistics operation, it has been possible to create a matrix, table number 4.1.
Table 4.1 : Objectives, drivers and facility for each phases in humanitarian aids
operations.
Sudden onset
Preparedness
disasters
Immediate
Support
Dismantling
Response
First Phase
Second Phase
Objectives
- Prepare to
- Assessment
- The supply
- The chain is
- Passage to
or situations
be effective
of demand
chain is
working at
a national
in future
needs
working, but is
100%, there
control in
still early to
are more
the
- Start up the
make a
information,
operations
supply chain
standardization
here we need
disaster
response
standardization
Drivers
Cost
Time
Time
Cost
Cost
Planning
Agility
Agility
Coordination
Planning
Coordination
Coordination
Constant Drivers
Facility
Quality, Security
Cost
Time
Time
Cost
Cost
optimization
optimization
optimization
optimization
optimization
Planning
44
Planning
In this matrix it is possible to understand each phases (preparedness, immediate
response, first and second phase of support and dismantling) in a humanitarian aids
operation. Now each phase is going to be explained.
4.2.1 Preparedness
“A successful humanitarian operation mitigates the urgent needs of a population with a
sustainable reduction of their vulnerability in the shortest amount of time and with the
least amount of resources” (Jahre and Heigh, 2008). For this reason, according to A.
Charles in 2010, the next phases will succeed if the response to the disaster won’t
improvised but it has to be prepared as well as possible to be effective, this is the most
important objective for the preparedness.
According to Van Wassenhove (2006), preparedness consists of five key elements that
have to be in place to produce effective results, and “these in turn lead to effective
disaster management” (Charles, 2010). They are:
1. Knowledge management
This is important to have a background about how move to response in a disaster, it
is possible learning from previous disasters: capturing, codifying and transferring
knowledge of logistics operations (Charles, 2010).
2. Financial resources
“Preparing sufficient financial resources to prepare and initiate operations and
ensure that they run as smoothly as possible” (Charles, 2010).
3. Human resources
Selecting and training people who are capable of planning, coordinating, acting and
intervening where is necessary. Many authors, such as Samii, Tomasini and Van
Wassenhove have demonstrated how crucial this problem is. The shortage of
qualified staff and the high level of turnover often have harmful consequences on the
management of crises (Charles, 2010).
45
4. The community
“Finding effective ways of collaborating with other key players such as
governments, the military, businesses and other humanitarian organizations is not an
easy task” (Charles, 2010).
5. Operations and process management
Recognizing logistics as a central role in preparedness. Then setting up goods,
agreements and the means needed to move resources quickly (Charles, 2010).
According to Van Wassenhove (2010) there are five kinds of flow that we should
consider during a humanitarian operation, these are materials, information, funds,
people and knowledge flows. Each flow has its own goals. And it is important, during
the preparedness phase, organizing the ground in the most effective way.
Table 4.2 : Goals per flow (Van Wassenhove, 2010)
Flow
Goal
•
Material
Cost, speed and quality
(Boxes)
•
Information
•
(Bytes)
•
Funds
Limited access, then overflow
Relevance: tool for coordination
Liquidity: going to soft bids to cash
•
(Bucks)
Needs-based prioritization
•
People
Getting staff to the field
(Bodies)
Knowledge
•
Making skills available to create solutions
(Brains)
All these goals are important for each phase that we are going to study.
46
4.2.2 Immediate response
According to Kovács and Spens. (2007) “the main problem areas of the immediate
response phase lie in coordinating supply, the unpredictability of demand, and the lastmile problem of transporting necessary items to disaster victims.”
Here, in this phase, it is possible find some very important drivers as agility,
coordination and time, these drivers guide the humanitarian aids operations during the
immediate response phase.
The first driver that we are going to study is the agility, indeed it is fundamental because
when you are operating in high uncertain conditions and when the stakes of the success
are human lives you can’t have a gap between capacity and needs and therefore it is of
prime importance to ensure an adequate level of service at lesser costs (Charles, 2010)
and time. The second important driver is the coordination. There is an abundance of aid
agencies focusing on relief after natural disasters (Long and Wood, 1995) and the
cluster approach is used for “diverse functions, including sheltering, logistics and water
and sanitation, can be viewed as an effort to achieve functional coordination” (Jahre and
Jensen, 2010) so we can understand that it is very important when we are in a
humanitarian aids operation where there are a lot of actors employed at the same time. It
is important to note that the coordination is a fundamental driver present in each of three
phases, from immediate response to the end of the support phase.
Both of the drivers we have seen are linked to the third and final driver: the time.
According to Martinez, Stapleton and Van Wassenhove’s research in 2010, models for
aids distribution have been developed by Long and Wood (1995), Wiswanatah and
Peeta (2003), Ozdamar et al. (2004), Barbarosoglu and Arda (2004), Craft et al. (2005),
Miller et al. (2006), Jia et al. (2007), Sheu (2007), Tzeng et al. (2007), Yi and Kumar
(2007), Campbell et al. (2008), Balzik et al. (2008), Lodree and Taskin (2008), Beraldi
and Bruni (2009) and Lee et al. (2009) with the objectives of minimizing time to
respond or maximizing demand coverage.
47
4.2.3 Support or disaster recovery
Sustainment is considered the phase when the aid and the aid infrastructure are
employed fully for the period of responding to the crisis, while the rump up (immediate
response) is when aid and infrastructure (assets and staff) are deployed to the area
(Maspero and Ittmann, 2008).
From this sentence it has been proposed of splitting in two phases the disaster recovery
as there are different objectives for each phase according to Marlin U. Thomas (2002)
“during this phase the nature of the operation and logistics requirements can change
with the tempo and operational conditions but the basic processes and structure remain
in place”, so the supply chain is working but in different modes:
•
First phase:
After the immediate responses, regional actors can begin to aid victims in the
location of their family and friends (Lamont, 2005). In the first phase the supply
chain starts to work at 100%.
•
Second phase: this phase could be called the reconstruction phase, according to
Kovács and Spens (2007), it is important as disasters can have long-term effects
on a region. In this phase we have more information about the disaster, indeed is
easier that we have a complete vision about the situation, this is important
because the disaster prevention plans need to be revised to include things that
have been learned from the current disaster (Thomas, 2003).
This sharing has been made following division in phases made by Kovács and Spens in
2007. They wrote that “aid agencies have created a phased relief response which
typically occurs in three phases: seven-day, 30-day, and 90-day. During the first phase
of the emergency, e.g. flyaway kits are provided. These can sustain up to 2,000 people
for seven days. The second phase involves sending family survival kits, which can
support up to 5,000 people for 30 days. The third phase is related to reconstruction and
it involves long-term rehabilitation”. The main difference from this ideas is about the
first two phases, indeed they are joint in an unique phase for more or less forty days.
48
This division brings to different objective and drivers for each phase:
•
During the first phase the drivers are agility, coordination and time as in the
immediate response phase. This happens because the supply chain is working,
but is still early to make a standardization because it needs more information
about the disaster. So in this situation we need agility, indeed supply chain
agility is usually defined as the ability to respond to unanticipated changes
(Sheffi, 2004). As we have seen in immediate response phase, second important
driver is the coordination, indeed even in this phase there is an abundance of aid
agencies focusing on relief after natural disasters (Long and Wood, 1995). Even
in this phase cluster approach is very significant and it is used for “diverse
functions, including sheltering, logistics and water and sanitation, can be viewed
as an effort to achieve functional coordination” (Jahre and Jensen, 2010). The
time driver is important as in the immediate response phase.
•
During the second phase we can find different drivers. Indeed in this phase, like
the previous phases, the coordination is a significant driver because there are
still a lot of actors, seen as agencies and organizations, present in the zone of the
disaster. The main difference is that in this phase the disaster happened a lot of
time before, estimable in forty days, and so we can have many information more
to find a standardization and so a cost minimizing, indeed Van Wassenhove in
2008 considered the cost optimization as not the primary objective for the others
phases when is most important the minimizing of the time.
According to Maspero and Ittmann, in 2008, “the aid and the aid infrastructure
are employed fully for the period of responding to the crisis”, for this reason it is
possible to say that the chain is working at 100% and it is possible to find the
cost optimization with a route standardization.
It is important to note how the standardization could be very important even during all
humanitarian operation long. Indeed according to Van Wassenhove (2008) the
definition of standardization is “using common design and processes as a way to shorten
49
lead time, reduces inventory levels and increase forecast accuracy”. It is important to
note that it’s possible to apply it to:
-
parts: common parts using across many products;
-
processes: that apply to different products;
-
products: customizing the product as late as possible;
-
procurement: to benefit from buying power or reduce the negative impact of
variability
-
information: to have standards of how information should be collected,
processed, shared and reported.
4.2.4 Dismantling
The dismantling phase is the ramp down in the Life span of a disaster in figure 4.1 and
it is “when assets are gradually reduced and withdraw from the area to be redeployed
elsewhere” (Maspero and Ittmann, 2008). It is important to note “how the ramp down
phase is not signal the end of the need for aid, and it is normal for developmental and
long-term aid to the ramp up in the area to complement the ramping down of the
emergency response” (Maspero and Ittmann, 2008). “In the ramp down phase, when
each individual agency is focused on managing the handover and exit, coordination will
still happen but only occasionally and, in a sense, by default” (Van Wassenhove, 2010).
According to Van Wassenhove (2010) in the last phase is very important “start to
looking at buying more relief goods locally”, this happens because it is important during
the dismantling to help the restart of local economy as it was before the natural disaster.
4.2.5 Constant drivers
As it is possible to see in the table 4.1 there are even some constant drivers in the
humanitarian aids operations. The drivers that we have found are Security and Quality.
The first one we are going to study is the security. According to Van Wassenhove
(2010) for this driver it is possible to make an example, the same we have made in the
introduction chapter. Indeed we can take the situation of humanitarian activities in
50
Serbian-controlled Bosnia on 1993. In that situation Sodako Ogata, High Commissioner
for Refugees (UNHCR), decided to suspend all the humanitarian activities in Balkan
area. The Sodako’s decision was taken because there weren’t safe conditions for the
humanitarian staff that was working in that area. The situation changed few days later,
safer conditions were created and the humanitarian aid restarted. The importance of that
decision anyway is basilar to understand the security driver. Indeed for all the
organizations it is important to let their operators work in safe areas, if this condition
doesn’t work it would be impossible for humanitarian organizations to work in the
humanitarian space and so humanitarian purposes.
The second constant driver considered is the quality. Indeed according to Dufour, de
Geoffroy, Maury and Grünewald (2004) experience of humanitarian crises of the 1990s,
notably in Rwanda (1994), have led to increasing awareness among aid agencies that Il
ne suffit pas de faire le bien, il faut le bien faire (according to French Enlightenment
philosophe Denis Diderot: ‘It is not enough to do good, it must be done well.’).
For these reasons there are tools as the Sphere Project handbook, Humanitarian Charter
and Minimum Standards in Disaster Response, or the alternative Quality Project, based
on The Quality COMPAS tool. These tools are used by humanitarian organizations to
‘improve the quality of assistance provided to people affected by disasters, and to
enhance the accountability of the humanitarian system in disaster response’ (Sphere
Handbook, 2003).
Furthermore there are others humanitarian drivers that it is possible to find in
humanitarian aids operations. According to Charles (2010) “humanitarian supply chains
need flexibility, effectiveness and responsiveness to enter the arena”. Even the
reliability is important seen as the quality of the supply chain. These drivers, jointly to
the others, can give an idea about how complex could be the humanitarian field.
4.2.6 Facility
As facility we consider all the operations or the services that we should meet if we want
satisfy the operation drivers. In the facility we have considered there are cost or time
optimization and planning.
51
Cost and time optimizations are made to meet the objectives and the drivers of each
phase. Time optimization could be used in immediate response phase and in the first
phase of the support while cost optimization could be used in second phase of the
support, dismantling and preparedness. It is possible to use even cost optimization when
we speak about time, as we will see after. This is possible putting time as a cost and
considering unsatisfied demand as a cost itself. As we have already seen, according to
Martinez, Stapleton and Van Wassenhove’s research in 2010, models for aids
distribution have been developed by Long and Wood (1995), Wiswanatah and Peeta
(2003), Ozdamar et al. (2004), Barbarosoglu and Arda (2004), Craft et al. (2005), Miller
et al. (2006), Jia et al. (2007), Sheu (2007), Tzeng et al. (2007), Yi and Kumar (2007),
Campbell et al. (2008), Balzik et al. (2008), Lodree and Taskin (2008), Beraldi and
Bruni (2009) and Lee et al. (2009) with the objectives of minimizing time and cost to
respond or maximizing demand coverage.
Another facility that we can use to meet the objectives and the drivers in humanitarian
operations is the planning. It has to be used especially in preparedness and during
dismantling phases. According to Kovàcs and Spens (2007) the first phase correspond
to strategic planning to prepare for emergency projects, and actual project planning
when the disaster strikes (Long, 1997). Just for example “Evacuation plans can be
developed and evacuation can be trained well in advance for such disasters” (Nisha de
Silva, 2001) and a lack of coordination often leads to confusion at the last mile (Murray,
2005). It is important to note how it is important even in dismantling phase that could be
seen as the beginning of preparedness phase.
In figure 4.4 it is possible to note the timescale of five phases in the sudden onset
disasters but it is important to understand that it is just an estimate indeed it is not
always possible be absolute in terms of the timescale of these five elements. Even it is
important to know that this timescale is different if we consider slow onset disasters, it
is possible to see an estimation of it in figure 4.3.
52
Figure 4.4 : Abstract of the chapter, the figure represent the lifecycle of a
humanitarian operation, the time scale and the table with the objectives, drivers
and faclity.
.
53
54
5 Methods
“The purpose of a humanitarian relief chain is to rapidly provide the appropriate
emergency supplies to people affected by natural and manmade disasters so as to
minimize human suffering and death.” (Balcik et al., 2008b)
Studying the humanitarian logistics operations we can note how much is important the
distribution of the aids in the last mile of the supply chain and how could be difficult
make it. It is due by many problems and by the high level of uncertain present in
humanitarian field. For these reasons the work has been developed following some
criteria and with some restrains that we are going to explain.
5.1 The phase of the model
At first it was important to understand the problem, the distribution, and all the drivers
present in it as we have done in the previous chapters. Understanding them, the second
step is to say in which part of the lifecycle we want to put our discussion because, as we
have studied in the previous chapter, each lifecycle phase has its drivers and its
objectives. The phase chosen for this paper is the first part of the sustainment phase, as
it is possible to see in the picture 5.1.
For this choice the model that we are going to develop will have as main objective the
minimization of the costs in a time optimization. So we are going to minimize the cost
associated to each routes that the trucks will follow jointly with the minimization of the
cost associated to unsatisfied and late-satisfied demand for different types of relief
supplies. This formulation consider the time like a cost and his minimization would be
the minimization of suffering and loss of life during a humanitarian aids operation.
55
Figure 5.1 : The phase where our formulation can be used in the humanitarian
operations lifecycle.
5.2 Data and assumptions of the problem formulation
According to Balcik et al. (2008b) the last mile distribution problem determines the best
resource allocation among potential aid recipients in disaster affected areas that
minimizes the cost of logistics operations, while maximizing the benefits to aid
recipients. More specifically, the last mile distribution problem determines delivery
56
schedules, vehicle routes, and the amount of emergency supplies delivered to demand
locations during disaster relief operations. The main problems in the application of last
mile distribution in humanitarian operations are about unpredictability of resources
(time, personnel, transportation) and the high stakes associated with relief operations
(suffering and/or loss of life).
In 2008 Huseyin Onur Mete and Zelda B. Zabinsky proposed a stochastic programming
model to select, in preparation for disasters, the storage locations of medical supplies
and required inventory levels for each type of medical supply. In this model we are
going to consider the distribution of humanitarian aids at an LDC to a number of
demand points in its service region. Following the idea of Balcik et al. (2008b) LDCs
are usually established post disaster in locations that have access to the affected regions
and they have commonly minimal interaction among them. An LDC may be a tent, a
prefabricated unit, or an existing facility (e.g., school, church, warehouse). We assume
that the location of the LDC is predetermined and its capacity is sufficient to serve its
service region.
According to Balcik et al. (2008) in this model we consider the demand as made by two
main groups, Type 1 and Type 2, with different demand characteristics. Type 1 items
are critical items like tents, blankets, terry cans and mosquito nets. This kind of demand
is usually very large at the beginning of the humanitarian operations and for this reason
could be difficult to deliver it. So it has been decided to put a penalty for each unit of
unsatisfied demand. We assume that no type 1 inventory is present at any demand
location. Type 2 items are different from Type 1 items, because they are consumed
regularly and their demand occurs periodically over the planning horizon (e.g., food,
hygiene kits). If we can’t satisfy it we can’t accumulate it, indeed the unsatisfied
demand is lost and penalty costs accrue for each unit of lost demand. In this case we
don’t consider the distribution of the water, indeed it needs different supply features.
According to Balcik et al. (2008b) we assume that the vehicle fleet is comprised of a
limited number of vehicles with different capacity, speed, and compatibility with
various arcs in the network. In the model just two types of vehicle are consider. We
57
assume that each vehicle can carry both Type 1 and 2 items and each vehicle can
complete multiple deliveries in a single planning period and each demand location can
be visited multiple times (with the same or different vehicles) in the same planning
period. Furthermore some roads may be unavailable due to the destructive effects of a
disaster, which would affect vehicle choice and delivery routes. We will use a factor (
) to explain which is the condition of each route as traffic or as physical condition,
this factor will influence the cost of a route.
According to Balcik et al. (2008b) the planning horizon is variable and unknown a
priori. We assume that the planning horizon begins once the LDC is able to begin
delivering relief supplies to demand locations and it ends when the demand for both
types of items are completed (or supply is exhausted). Therefore, the planning horizon
parameter used in our model will be the worst case estimated, and it will terminate
when the delivery of relief supplies will be completed. We will construct a plan for
long-term relief distribution based on available information, but only execute the plan
for the coming planning period. The plans will be updated at the beginning of a
planning period if new information will be obtained regarding resource or demand
levels for any future planning period. The planning period will be determined by
decision-makers according to the characteristics of the problem. For ease of
presentation, we’ll assume the planning period will be one day in the problem
formulation.
58
6 Last mile distribution
“Last mile distribution is the final stage of the relief chain; it refers to delivery of relief supplies
from LDCs to the people in the affected areas (demand locations).The most significant logistical
problems in the last mile generally stem from the limitations related to transportation
resources and emergency supplies, difficulties due to damaged transportation infrastructure,
and lack of coordination among relief actors. It is challenging for relief agencies to develop
effective and efficient distribution plans in such a complex environment while simultaneously
achieving a coordinated response.” (Balcik et al., 2008b)
Transportation is the major component of disaster relief operations. Post-disaster
transportation, especially across the “last mile”, can be particularly challenging for
relief agencies. The challenge arises from damaged infrastructure, limited transportation
resources and the sheer amounts and bulk of supplies to be transported (Balcik et al.,
2008b). According to Balcik et al. the task is made more difficult for the strict financial
and time limitations, making cost-efficient vehicle routing decision important. For these
reasons we are going to study the Last Mile distribution problem and its application in
the Haitian case.
6.1 Structure of the relief chain in Last Mile
The structure of the supply chain in the Last Mile is very important because, knowing it,
it is possible to understand how the aids move in the humanitarian operations and which
are the places where aids arrive, or are stored, sorted, transferred and which are the
local distribution centers where aids are distributed to beneficiaries.
59
Figure 6.1 : Structure of the relief chain (Balcik et al., 2008b)
The logistics situation that we can typically find in a disaster relief operation is seen in
figure 6.1. Indeed it is possible to find at first a primary hub, usually seaports or
airports, where humanitarian aids arrive from all parts of the world. The second step
could be a central warehouse. In this place the humanitarian aids are stored, sorted and
transferred to tertiary hubs. These tertiary hubs usually are local and temporary
distribution centers. The tertiary hubs are even called LDCs, local distribution centers,
and they are used to deliver aids to beneficiaries.
6.2 Introduction of the problem
In this formulation we are going to consider a last mile distribution system. In this
humanitarian operation there is a LDC store that distributes emergency relief aids to a
number of demand locations using a fixed set of vehicles. We want to determine a
delivery schedule for each vehicle and make inventory allocation decisions considering
suppliers, vehicle capacity, and delivery time restrictions (Balcik et al., 2008b). To
make it we want to minimize the sum of transportation costs (that consider even
transportation times) and penalty costs for unsatisfied and late-satisfied demand for
different types of relief supplies.
60
6.3 Literature review
In the history of humanitarian logistics operations it is possible to find many examples
of models for aids distribution. According to Balcik, Beamon, Smilowitz’s research in
2008 the most important steps considered in development of Last Mile distribution are
due to some researches.
It is possible to start with the literature review considering the Knott’s researches in
1987 and in 1988. He wanted to minimize cost as transportation or maximize the
number of humanitarian aids beneficiaries in food deliveries. The next step was made
by Haghai and Oh in 1996. They introduced in the problem formulation the time
window. Indeed they wanted to minimize cost in transportation field, for example costs
as vehicular or commodity flow, or costs as supply/demand carry-over and transfer
costs, considering the time period in which the transport operation is planning. After
some years in 2002 and in 2004 Barbarosoglu et al. studied operational scheduling in
helicopter operations in humanitarian aids operations. They didn’t take it as a singular
problem, indeed they split it into two sub problems, in which at first were made tactical
decisions while in a second step were taken operational routing and loading resolutions.
In the same years Ozdamar and colleagues, in 2004, studied a humanitarian logistics
problem considering the distribution of multiple commodities from some supply centers
to place closed the disaster areas. The aims of their model were to determine the
scheduling of deliveries, with the quantities of loads for each route. They wanted to
minimize of unsatisfied people over time and so they wanted to minimize the suffering
for the people in the emergency relief operations. Another step was made by Angelis et
al. in 2007. Indeed they studied for the World Food Programme (WFP) a scenario with
more than one depot, routing and scheduling for many vehicles with different features
in a humanitarian aids logistics operation.
At the end in 2008 Balcik et al. developed a model that considered different kinds of
demand in the last mile distribution contest, a first study was made in 1987 by Knott but
it wasn’t developed analytically, that considered the complexity of the environment and
the uncertain of the humanitarian operations.
This study differs from the studies that we have seen because it considers even the
status of the routes and their traffic situation when it is important to find the list of
61
candidate routes, furthermore in this study we are going to consider the penalty cost as
directly proportional to unsatisfied demand, deliver by deliver.
Table 6.1 : References of the literature review
Approach
Notes
Knott
References
1987
1988
Operations research
heuristics with artificial
intelligence techniques
Haghai and Oh
1996
1997
Heuristic algorithms
Barbarosoglu et al.
2002
Mixed integer
programming model
(MIP), that is solved
using an iterative
coordination heuristic
The LP determines the number of trips for
each camp to satisfy demand. The
objective is to minimize the transportation
cost or maximizing the amount of food
delivered.
This is a multi-commodity, multi-modal
network flow problem. There is even a
time window. The objective is to
minimize the sum of the vehicular flow
costs, commodity flow costs,
supply/demand carry-over costs and
transfer costs over all time periods
considered.
The problem is decomposed hierarchically
into two sub-problems where tactical
decisions are made in the top level and the
operational routing and loading decisions
are made in the second level for helicopter
activities.
Including relief network uncertainties
The model expects a logistics problem for
distributing multiple commodities from a
number of supply centers to distribution
centers
The authors set a service level for food
distribution and developed a linear
integer-programming (IP) model that
maximizes the total satisfied demand.
The model integrates decisions about the
location and inventory decisions. It
considers multiple item types and it
captures budgetary constraints and
capacity restrictions.
It has been developed analytically a model
that considered different kinds of demand
in the last mile distribution contest.
furthermore it considers the complexity of
the environment and the uncertain of the
humanitarian operations.
Ozdamar and
Colleagues
2004
2004
Algorithm and heuristic
Angelis et al.
2007
Linear integerprogramming (IP)
Balcik et al.
2008a
Mixed integer
programming model
(MIP)
2008b
62
6.4 Model formulation
To make the model easier and more understandable, according to Balcik et al. (2008b)
we can split the approach into two phases. Indeed we can consider a first phase to study
the routes and the cost and the time associated to them. This is feasible taken the sets of
demand and the set of the vehicles, with their own features (as velocity or time to loadunload), and the matrix with the distances among each point to find the list of candidate
routes. So we can have the routes with their cost and duration and the set of demand
locations visited on each route.
The second phase is the last mile phase. In particular in this phase it is important to
consider penalty costs features of each vehicle, for example when the capacity is less
than the demand for each item in each location. The aim of this second step is to find
the routes for each vehicle in the next time window and the amount of each item to be
sent to the demand points minimizing the costs associated to unsatisfied demand and to
the routes.
In this section we are going study the mathematical formulation of the problem and how
they have been studied, considering some aspects that can be used later in humanitarian
logistics aids operations.
6.4.1 First phase
In this phase we are going to study all possible delivery routes for each vehicle. The aim
is to find all the possible candidate routes of the second phase.
The first step for our model is to understand which could be the possible routes among
the distribution points, it is possible to find them from a graphic as in figure 6.2. As
distribution points here we consider all the points in the disaster area where we want
deliver humanitarian aids to beneficiaries.
63
Figure 6.2 : Graphic with the set of the possible distribution centers in the
humanitarian operation.
The second step is to build a matrix with all the minimal distance between these
distribution points. It is possible to make it using an heuristic algorithm. We can find a
matrix as in table 6.2.
Table 6.2 : Distances (in Km) between the nodes of the graph in figure 6.2
Distance Km
A
B
C
D
E
A
0
B
C
D
E
0
0
0
0
According to Balcik et al. (2008b) some roads may be unavailable due to the destructive
effects of a disaster, which would affect vehicle choice and delivery routes. We will use
a factor (
) to explain which is the condition of each route as traffic or as physical
condition, this factor will influence the cost and the time of a route (see the table 6.3).
64
Table 6.3 : Conditions of each route.
Condition
A
B
C
D
E
A
0
B
C
D
E
0
0
0
0
The values of this matrix, table 6.3, can be chosen in function of physical condition of
the route or in function of the traffic. These values are known at the beginning of the
day. The values can be seen in table 6.4. This factors are used during the first phase to
understand the real time and cost associated to a road. The real road time is:
=
∗
and so it is possible to find the real cost for each route. The cost for each route will be
found considering a factor that is the cost per unit of time and per kilometer.
Table 6.4 : An example of factor (
) that could be taken to explain the traffic or
the physical condition of the routes and that will influence the time and the cost of
the route.
Factor
Good practicability
1
Medium practicability
1,5
Low practicability
2
Very low practicability
5
Impracticality almost complete
10
Impracticality
-
After the second step we are able to find all the routes. We consider that all routes start
in A, LDC established post disaster and which capacity is sufficient to serve its service
region, and finish in the same A. So we can find all the 625 possible combinations of
route for ours four distributions center and a local distribution center (LDC).
65
The lengths of the routes (
) have been found as the amount of the distances between
the nodes “i” touched by the route “r” we consider. It is possible to see this in the next
formula:
(1a)
=∑
( )
∀ i ϵ N(r)
where “i” is the set of all demand locations.
Table 6.5 : Routes matrix.
Name route
Aa
Aba
Aca
Ada
Aea
Acba
I
N
-
In the routes matrix we can see all the possible routes that can be used by our vehicles
during humanitarian aids operations. Some of them could be repeated and so we should
delete them, taken the less cost ones. After this steps we are going to have some
candidate routes that can be used in the distribution optimization.
In the third step we consider the features of the vehicle, as velocity and time to load and
download, jointly we consider the cost for the routes for the transport for each vehicle.
In the model just two models of vehicle are considered, with different speeds, costs and
capacities.
At the end we can find a list of candidate routes and for each route: the name (with the
set of demand locations visited), time and cost for all of vehicles we consider.
66
This phase is over and, according to Balcik et al. (2008b), it has to be executes again
just if there are structural changes in the network and vehicle fleet.
6.4.2 Second phase
Now we have all the candidate routes. The next step will be to determine the scheduling
of deliveries (when take a route and with which kind of vehicle), the quantities of loads
for each vehicle of the routes, the quantities of loads for each type of item. For these
reasons it is important to make a model as the model we are going to study.
The base idea for this model has been taken by “Last mile distribution in humanitarian
relief”, Balcik, B., Beamon, B.M., Smilowitz, K., 2008. It was developed considering a
different penalty cost for the unsatisfied demand and considering that all the vehicles
used are two types of vehicle with different features.
The final objective of this model will be to minimize the cost associated to each route
jointly the minimization of the cost, penalty cost, associated to unsatisfied demand. This
has been studied to minimize the time, seen as cost, in the distribution minimizing the
sufferings and lives lost for the aids beneficiaries.
At first we are going to assume the planning period will be one day in the problem
formulation. This choice is made because the period we want to study is considered a
period with a high uncertainty and with a lot of new information each day. For this
reason it could be impossible to try to made a plan for a long time horizons.
The second data we need are the set of candidate routes that we found in the first phase.
These are all the routes that we can use in this second phase and for each of those we
know the sets of demand they touch and the time and the cost associated to them for
each kind of vehicle. For this reason in the theory we should consider even the set of
vehicles present in the disaster area because they can have different features, for
example capacity and velocity and costs, and for these they can have different impacts
in the humanitarian relief operation. Furthermore different types of vehicles can be used
in different zones in function of the landscape situation.
67
As we have seen in the first phase, in the second phase is important to understand where
are the demand points, set of all demand locations, because these will be all the points
in the disaster area where humanitarian operators have to supply the humanitarian aids.
At last we should consider which types of items are requested in this demand points,
knowing that there are 2 types of demand which different features.
Routing and vehicles parameters
There are some parameters that we have to consider. At first we should consider
parameters as cost and time (as a fraction of a day) of each route for each kind of
vehicle. These differ each other because each vehicles has different features and so
different impact in the logistics operations. The matrixes with this kind of data are the
outputs of the first phase.
Another feature that it is important to consider is the capacity of the vehicles that are an
input for the second phase. This is important because the capacity of the vehicles is not
equal to infinity and so they have this constrain.
Demand parameters
As well as the demand points, set of all demand locations, demands of type 1 an type 2
in each locations are very important. The real problem is that these two parameters are
unknown a priori and for this reason it is important to understand the distribution of the
population to understand and quantify them.
Such discourse could be done for the cost factor for unsatisfied type 1 and 2 demand at
each location. In the model we study we consider the penalty cost as in function of the
distribution of the damage. So the costs will be more expensive in those zones where
has been the major damage, this is used to parameterize the worst situation in which the
beneficiaries are living.
68
Delivery and Routing decision variables
In the model there are even some variables we have to consider. We call them Delivery
and Routing decision variables. The first one is the amount of demand of type 1 or 2
delivered to the location in one day. This variable is very important because by that we
can know the fraction of unsatisfied demand, for each type of demand, at the location
we are interested studying even the inventory level of the item 2 in the location, because
the item 2 can be stored.
So the last variable we are going to study is a variable we can call X, and it says us if
the route is used or not. Studying this variable we can know, in the end, which are the
routes we will use in a day for the supplies.
Objective function
As we have seen in this chapter we want to find all the routes that can minimize the
total cost for the operations and that can maximize the number of the deliveries and so
the number of beneficiaries.
(1b)
( ) =
(
!" #
+
%
& "' )
In the simple formula (1b) it is possible to see the objective of ours minimization, a
function with both the cost of the routes and the cost for the penalties due to unsatisfied
demand. The cost for unsatisfied demand is due to the difficulties linked to the
deliveries, indeed sometimes it is possible to have too many deliveries to satisfy them
during the window time. This cost is associated to the damage, seen as a cost factor, and
the people who need this kind of delivery. It is possible to understand this formula in the
next paragraph when the objective function (1c) is explained. Furthermore the route
costs are taken by the duration of each routes for a factor that considers how much is the
cost per second and per kilometer.
69
The formula (1c) has some constraints. Indeed it has to follow some structural and
logical limits of the system. At first the supplies have to be less or equal than the
quantity of the items present in the LDC, it is impossible to deliver more than the
quantity that we have in the warehouse. Furthermore the deliveries have to consider the
capacity of the vehicles and so there is a limit in the quantity delivered each day.
Another important constraint is the time. Indeed we consider the time to deliver as a
fraction of a day and so it is impossible to take routes for more than one day. When we
consider more deliveries in a day we don’t want that they take more time than a day.
More constraints are considered just in the mathematical model.
At the end of this second phase we have all the routes that our set of vehicles will make
each day to deliver the quantity of aids. We want to minimize the objective function we
have seen.
This second phase, according to Balcik et al. (2008b), has to be used for the coming
periods until new information become available.
6.5 Mathematical formulation
6.5.1 Objective function
(1c)
((()
. "∈- *∈,
•
•
)
/
*
*
/
"*
+ ( ( ( 0 "1 "
25(6) "24
23
is the cost of route r for vehicle k є K.
"*
is the routing decision variable. It is 1 if the route r є R is used by the
vehicle k є K on the day t є T, otherwise it is 0.
•
7 " is the penalty cost factor for unsatisfied type e demand at location i є N on
the day t є T.
•
70
1 " is the unsatisfied type e demand at location i є N on the day t є T.
6.5.2 Constraints
(1d)
1 8" = :;8 − (
:
•
•
"
( ( @8 >* A
( ) >?8 * ,
∀ i ϵ N, t ϵ T
; is the demand of the type e at location i є N.
@
>*
is the amount of the type e demand delivered to location i є N on the day t є
T by the vehicle k є K via route r є R.
(1e)
1 B" = :;B + C B,"E8 − (
:
•
( @ B"* − C B" A
( ) * ,
∀ i ϵ N, t ϵ T
C B" is the inventory level of the type 2 demand at location i є N at the beginning
of day t є T.
(1f)
(
( ( @8 "* ≥ ;8
: 25(6) *2F "24
∀iϵN
(1g)
"
( ( ( (@
2H
•
25(6) >?8 *2F
>*
"
≤ ( J>
>?8
∀ t ϵ T, e ϵ E
J> is the amount of the of the type e є E relief supplies arriving to the LDC at
the beginning of day t є T.
71
(1h)
( (@
25(6)
•
23
"*
≤ K* /
"*
∀ r ϵ R, t ϵ T, k ϵ K
L* is the capacity of the vehicle k є K.
(1i)
(/
2H
•
"* M *
≤1
M * is the time to make the route r є R by the vehicle k є K.
∀ t ϵ T, k ϵ K
(1j)
0 ≤ 1 " ≤ 70
•
70 is the number of the beneficiaries need the aids.
∀ i ϵ N, t ϵ T, e ϵ E
(1k)
C B8 = 0
∀iϵN
(1l)
C B" ≥ 0
∀ i ϵ N, t ϵ T
(1m)
@
72
"*
≥0
∀ i ϵ N, r ϵ R, t ϵ T, k ϵ K, e ϵ E
(1n)
/
"*
= P0,1Q
∀ r ϵ R, t ϵ T, k ϵ K
Table 6.6 : Main differences between Balcik et al. and Peretti model.
Factor of route
conditions
Penalty cost for
unsatisfied demand
Factor of decision
Balcik et al. (2008b)
No Present
R"
It is a fixed factor, it is the
maximum penalty cost for
each day and each item. The
factor is studied a priori and
considers the percentage of
unsatisfied people and the
cost for all whole people in
the area
Peretti (2011)
0 "1 "
Penalty cost is proportional
to the people (S) that need
the aids for penalty cost (P)
associated to each person.
The main factor considered is
They decide how deliver
0 " 1 " , that studies all the
following the parameter R" ,
beneficiaries (S) that need
that is fixed and doesn’t
the supplies and their
change during the day.
suffering level (P) considered
as a cost, the factor changes
after each delivery because S
changes.
73
74
7 Haitian case
“The impact of a disaster depends not only on the nature and the intensity of the disaster, but
also on its location. The same earthquake around San Francisco, where buildings are built with
specific materials and local resources are prepared to respond, will not have the same impact
as one that strikes a poor, unprepared country such as Haiti […]. It also depends on the
population density”. (Charles, 2010)
On January 12th 2010, at 16:53 local time, an earthquake measured 7.0 of magnitude on
the Richter scale hit Haiti. This caused a high level of destruction that required an
elevate effort for the humanitarian operators to respond.
Figure 7.1 : Map of Haiti where it is possible to see the most important cities.
This situation was defined as “the largest humanitarian operation ever carried out in a
single country” (IFRC 09/02/2010). It was reported the urgent need of many supplies as
medical facilities, food, clean water, emergency shelter and logistics and
75
telecommunications because after the disaster there were many victims and huge
damages (logcluster.org).
Table 7.1 : Global Figures about Haiti earthquake in January 2010
( http://europa.eu/ ).
Global Figures
222.750 people dead
1.7 million people left homeless
60% of hospitals destroyed
Around 5000 schools destroyed (23% of the total of Haiti)
3 million people affected (nearly one third of the population)
Total impact cost around USD 7.8 billion (€5.9 billion)
According to the European Union (http://europa.eu/press_room/pdf/factsheet_haiti
_en.pdf), the capital was destroyed, the infrastructures were devastated and the
government and humanitarian agencies on the ground lost a lot of staff, resources and
facilities. Post-earthquake aid therefore had to be delivered in an environment
characterized by numerous logistical, infrastructure, social and political challenges.
For these reason we have chosen the Haitian case to face the discussion about the
importance of an optimization in the humanitarian aids operations. All the challenges
presented in this humanitarian operation, for example logistics ones, are subjected to
many constraints that usually are not present in the commercial supply chains.
7.1 Infrastructures and damage
Haiti was considered the poorest country in the western hemisphere (Pedraza Martinez
et al., 2010) because the 80% of the population, more than 9 million inhabitants, lives
with less than two dollars per day and the unemployment rate was almost 70%.
Furthermore it was subjected to other disasters in the last years, just for example six
hurricanes since 2007.
76
The poor quality of the Haitian infrastructure were considered insufficiency. Indeed
many building were made with not conforming construction materials and they were
without foundations. Furthermore according to CIA World Fact Book, almost 76% of
4160 Km of roads were unpaved in the country. At last, according to IFRC only 50% of
the population had permanent access to clean water.
After the natural disaster in Haiti it was possible to find an high level of damage.
According to IFRC the earthquake destroyed approximately 75% of the building in the
capital, Port au Prince, and Leogane and it destroyed more or less 40-50% of the
buildings in Carrefour and Jacmel. Many others problem were due to the condition of
the roads. For example the main roads to go to the capital coming from the seaport, the
airport and the Dominican Republic, just not in perfect conditions before the disaster,
were destroyed by the seism. Indeed the earthquake made the transportation network not
operational. In the destroyed buildings 60% of the existing hospitals were considered.
Another problem was due to the bottlenecks. Indeed the days after the disaster it was
possible to use just few infrastructures, as routes or hospitals or airports, a so they were
completely supersaturated.
This high level of damage in infrastructures and the high level of bottlenecks concurred
to increase the difficulties during the operations because the humanitarian operators
couldn’t easily find positions to place for example hub or distributions centers or good
candidate routes for the distributions.
As it has been said in chapter 1, the area of humanitarian aid operations is a particular
field, full of difficulties, in which we can find logistics operations: the Haitian case can
be a perfect example of this kind of situation.
77
Figure 7.2 : The map of Haiti with the epicenter of the earthquake. It is possible to
see how closed it was to the capital Port au Prince
(https://www.cia.gov/library/publications/the-world-factbook/).
7.2 Port-au-Prince
Port au prince is the capital and the most important city of Haiti. In this city 900.000
person live with a high density, more or less 25.000 people each square kilometers. The
epicenter of the earthquake was registered almost 25 kilometers far from the capital.
The direction was west-southwest. It is possible to see the situation in the figure 7.1.
This city has been chosen for its features. Indeed, in the disaster area, it is the biggest
city with all the needs and the typical problems of a humanitarian operation. After the
disaster indeed there was a high level of damage with a lot of victims and homeless’. In
a situation like this it is possible even to consider all the typical hubs present in the
humanitarian supply chain and find all the features that we have studied in the previous
chapters.
78
7.2.1 Haiti supply chain
In the humanitarian operation in Haiti, according to Pedraza Martinez et al., we know
that WFP, leader of the logistics cluster, supports relief operations in the Central
America region with a logistics hub in San Salvador. For this reason we can consider
San Salvador as the primary hub in this humanitarian operation. As secondary hub it is
possible to study one of the three access points to the Port au Prince. These access
points are: the airport, the seaports and the roads. The choice has been focused on the
airport. It could be considered even the seaport, but it has been decided to consider the
airport because in the first phase of sustainment phase it was available and its capacity
as enough for the deliveries we want consider. Indeed it should be given importance that
studying official WFP documents it is possible to note that for the first days after the
natural disaster the sea port was damaged. The others access points to Haiti, according
to Pedraza Martinez et al., are two roads that connect Santo Domingo to Port au Prince
but they have an average time travelling of 8 hours and very limited refueling capacity
after crossing the border into Haiti.
It is possible to choose the first two hubs in function of the infrastructures that has been
made in the preparedness phase (for example San Salvador hub) and that we know can
support the efforts during all the operations (for example Airport or Seaport hub when it
will be operative).
So, as it is possible to see in the chapter 6.1, the structure of the supply chain in the Last
Mile is very important because, by knowing it, it is possible to understand how the aids
move in the humanitarian operations and which are the places where aids arrive, or are
stored, sorted, transferred and which are the local distribution centers where aids are
distributed to beneficiaries.
79
Figure 7.3 Humanitarian supply chain in Haiti.
80
8 Discussion chapter
“In the wake of the devastating earthquake in Haiti, one of the aftershocks facing the country is
not seismic in nature, but concerns the struggle to coordinate relief efforts in order to reach
those in need most effectively.” (Fritz Institute, 2005)
In the next section the case of Haiti in Last Mile logistics operations will be studied.
The Last Mile and Humanitarian features, we studied in the previous chapters, jointly
with Haitian ones will be faced together.
8.1 Demand points in Haiti
The first things that we want to study in a humanitarian logistics operation are the
demand points. Indeed it is very important to know where are the positions of the
demands because in the next steps they will influence all the decisions, as just for
example the positions of the LDC or the quantity of the infrastructures we need and the
distribution points.
The quantify demand operations are ones of the most important and difficult in a post
disaster situation. Anyway it is possible to study the disaster jointly with the distribution
of the people in the area to find the distribution of the demands.
81
Table 8.1 : Legends of the figure 8.1 to see the damage and the distribution of the
population in the zone of Port au prince (http://ec.europa.eu/).
If we consider the case of Haiti, and the Port au Prince situation, we can study all the
information about the city, building and roads for example, jointly to the natural disaster
position to understand which could be the areas with the worst damage in the city.
Analizing it, it is possible to study the population. We are helped by the census made in
2003 (made after one of the hurricanes that hit the island and that made a lot of damages
in the country) and by the WFP that cure the logistic cluster and its website
(www.logcluster.org). In this case we can focus on Port au Prince, as it is possible to see
from the figure 8.1, the area with the wrost damage, so we can know more or less which
are the areas that need more supplies.
82
Figure 8.1 : Image of the damage and the distribution of the population in the zone
of Port au prince (http://ec.europa.eu/).
After this step, where it is possible to know where are the demand points, there is
another important and critical point. The point is to know how much is the demand of
each type of item in each location. This is critical because it is very difficult to
understand it in this phase because there are not so many information. One of the
answer that it is possible to use is to study the population that lives in the area in
function of the damage. Knowing it, studying the number of the people living in the
area it is possible to approximate the beneficiaries that need relief. (www.geohive.com)
This data will be very useful, because, as we have seen in the Last mile distribution
chapter, number 6, it is possible to use it as the data about the demand of aids to know
how many deliveries have to be done to the demand point and the volume of these
deliveries.
83
Another important step in Haiti for the distribution is the quantification of the penalty
cost for unsatisfied demand. The main reason of this step is that sometimes the
infrastructures are not enough to satisfy the demand. In the case of Haiti, the days and
the weeks after the earthquake, many infrastructures were damaged and they can’t work
fully, for example the seaport, the roads and distribution infrastructures. For this reason
it is important understand which is the level of the emergency in each demand point and
after that it is important put the right cost for each situation. Doing this, it is possible
make a sort of priority of the deliveries. Indeed if we consider all the demand points we
will study even the level of suffering and dying that could happen if we don’t deliver
the aids at the right time in the right place. In the case of Haiti the prioritization could be
made in function of the number of the people that need relief, because it very hard to
find information about the distribution of the damage without any meetings or visits
around the earthquake area.
This step is very both important and critical. Indeed if the costs are quantified in the
wrong way all the distribution can be wrong.
8.2 LDCs location and capacity
In the situation in Haiti it is possible to decide the LDCs position considering the
distribution of the population and the capacity, in terms of transport and storage. Indeed
it is important that the LDC has been placed in a place directly and easily refillable by
the secondary hub.
In Haitian case we consider as the tertiary hub in Port au Prince a hub closed to the
airport (secondary hub) and we assume that its capacity is sufficient to serve the city. If
it was not enough, it is important to study others centers with the right features to serve
the city.
As we saw in the previous chapter, here it is important, starting from the demand
distribution, to understand which kinds of features Local Distribution Centers should
have. The features we need are the position and the capacity of these hubs. For the
position it is possible to decide it studying the distribution of the demand and assuming
that its capacity could be sufficient to serve the region. But the problem is more
84
complex. Indeed, according to Balcik et al. (2008b) the selection of a LDC has to
consider many factors, for example security and safety and transportation infrastructure.
For these reasons, the positioning of the LDCs is an important problem and it has to be
studied jointly and deeply with all the operation because it affects the performance of all
the distribution operations.
8.3 Distance matrix
From the figure 7.4 it is possible to understand where is it focused the demand. Indeed
five points have been found in Port au Prince, visible in figure 7.5: Airport (A),
Carrefour (B), Torgeau (C), Pétionville (D) and Delmas (E).
Figure 8.2 : Demand points in Port au Prince.
The figure 8.2 is one of the steps to find the matrix of distances as the table 6.2. Indeed,
using a program such as Google map, it is possible to know all the distances between
the nodes in Haiti. These are sure data, indeed they are taken by the real status of the
roads in Port au Prince by Google map program.
85
Table 8.2 : Distances (in Km) between the nodes of the figure 8.2 about demand
points in Port au Prince (www.maps.google.it).
Distances Km
A
B
C
D
E
A
0
13
7,5
4,3
8
B
13
0
8,6
16,6
14,1
C
7,5
8,6
0
3,2
6,1
D
4,3
16,6
3,2
0
4,5
D
8
14,1
6,1
4,5
0
From this matrix it is possible to find all the routes, with the time and the cost for each
route. For the time of the routes it is also important to consider load and download time.
Next step would be to consider the routes conditions. It is very important in an
operation like Haiti, indeed the status of the roads could be often not so good.
Table 8.3 : Estimated route conditions in Haitian case.
Conditions
A
B
C
D
E
A
0
1,5
2
1
1,5
B
1,5
0
1,5
1,5
1
C
2
1,5
0
2
2
D
1
1,5
2
0
1
E
1,5
1
2
1
0
This factor allows to balance the costs and the times in function of the condition of the
roads. Indeed if a road has a low practicality the time to make it has to be different by
the time to make it in normal conditions. In this case this factor has been estimated by
some news about the roads taken by Logistic cluster website (table 8.3).
Considering all these factors it is possible to find all the candidate routes in the area that
can be used for the distribution, they are 64. In the list of candidates routes we can
delete the routes with high cost. Indeed all the routes with high cost have even hightime path. So it is possible to find 16 routes that we can consider as candidate routes.
It is important to note how usually, when we have to deliver a high quantity of aids, the
most important routes are the direct routes. Indeed when the quantity of items to deliver
86
is high it is better to supply them directly and try to make more direct deliveries than
more-nodes ones.
8.4 Last mile features in Haiti
In Haiti earthquake one of the biggest problem was the uncertain of the data. This
happens when the data changed quickly because there are always more information. For
this reason it is important to consider a not long time horizon. And it is important to
understand how much it could change in just few days.
Indeed in this paper it has been decided to take a time horizon one day long, and
considering all the deliveries even for the days just in case of no-new information.
The aids that needed Haiti were, according to Pedraza Martinez et al., both food,
medical and shelter-no food items. For this reason it is possible to consider them as two
main groups of demand, Type 1 and Type 2. Type 1 items are critical items like tents,
blankets, terry cans and mosquito nets (shelter-no food items). And if there is a new
demand of this type of demand it is possible to put in the model as one day more
demand with Type 1 features. Type 2 items are different from Type 1 items, because
they are consumed regularly and their demand occurs periodically over the planning
horizon (e.g., food, hygiene and medical kits). The increase of this kind of demand
would say that it has been delivered each day until the end of the crisis.
In documents taken at the web site www. logcluster.org it is possible to note which
kinds of truck have been used in humanitarian logistics operations in Haiti and how
many vehicles are present in the area that could be used for the logistics relief
operations. These vehicles have different features. It has been decided to consider only
two kinds of vehicles, with different features. This simplification has been taken to
make the comprehension easier than considering all of the different vehicles that could
change at each time. But it is important to note that the different features are considered
and this has to be seen just as simplification in the management of the distribution.
87
8.5 Program proposed
As it has been seen in the previous paragraphs, there are many critical features in
humanitarian logistics operations and it could be very difficult to manage them during
the operations without a well-planned plan or a very well organized intervention. For
this reason is going to propose a program to suggest a better distribution in a so critical
environment. Indeed in the humanitarian operations, as we have studied in previous
chapters, one of the most critical driver is the time and it is very important to optimize it
jointly with the maximization of the relief distribution to the beneficiaries. It is possible
to see the Program as attachment 1 at the end of the script.
8.5.1 Program specifications and drivers
The most important diver used by the program to choose the distribution point is the
“Coeff_maxperdita” that consider the number of people that need the specific items in the
distribution points before the distribution ( “Qdd” ) and the critical level of the
population associated to the penalty cost ( “P” ). The critical level of population is a
factor associated to the level of damage in the area and to the level of population
suffering in the area. The driver “Coeff_maxperdita” continues to change after each
deliver. Indeed after each delivery the number of the people that need the specific item
in the distribution points decreases because some of people are satisfied by the deliver.
A second but very important diver used is the time for each route ( “T” ) and the total
time in a day ( “Time_total” ). Indeed the program in a first step considers the
maximization of the distribution and in a second step it considers to use the shortest
route possible as to satisfy the maximization of the distribution chosen, so it takes all
the routes that can deliver in the distribution points we need and it chooses the most
efficient one. Making this it is possible to have the maximization of the distribution
using the time as less as possible.
The others constrains considered are the times, the capacities and the vehicles features,
these are important because they considers the limits of the infrastructures that we have
and some humanitarian safety limits. For example it is not possible to deliver during the
night hours.
88
8.5.2 Program Input
The data that has to be upload to make work the program are the times and the costs for
each type of vehicle it has been considered to make all the parts of the routes to deliver
the items.
8.5.3 Program Output
The output of the program is a matrix that says in which demand points the vehicle has
to go, which route it has to take and how much (in volume) item it will deliver for each
demand point. It is possible to see the table 8.4 to understand the output of the program.
Table 8.4 : Program output matrix.
Schedule of
Delivered
Delivered
Delivered
Delivered
Route
deliveries
in B
in C
in D
in E
chosen
8.6 Data Input
It has been considered the Haitian case with some data. In this simulation the main
features considered are:
•
All possible routes that can be touched by the vehicles for the five demand
points taken in the previous chapters (table 8.5);
89
Table 8.5 : The numbers and the names of the main routes used during the
distribution in Haiti.
Number of
the route
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
•
Route
name
ada
aca
adca
aea
aeda
aeca
aecda
aba
acba
adcba
abda
aeba
aecba
adeba
adecba
A
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
B
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
C
0
1
1
0
0
1
1
0
1
1
0
0
1
0
1
D
1
0
1
0
1
0
1
0
0
1
1
0
0
1
1
E
0
0
0
1
1
1
1
0
0
0
0
1
1
1
1
Penalty cost associated to the unsatisfied demand. Thus it is important to study
the level of damage in the area to understand which is the importance of the
delivery. It has been studied the situation after the earthquake in Haiti and it has
been taken the level of damage for each area we have considered. The real level
of the damage is unknown and so the penalty costs have been estimated (table
8.6) by the data have been taken by the European Union website
(http://ec.europa.eu/);
Table 8.6 : Estimated penalty cost for unsatisfied demand in Haitian case.
Penalty cost
90
Unsatisfied
Unsatisfied
Unsatisfied
Unsatisfied
demand in
demand in
demand in
demand in
B
C
D
E
1.3
2.1
1.5
1
•
Distribution of the population in Port au Prince with the number of the people
need humanitarian aids in each demand point has been considered and esitmated
by the European Union website (http://ec.europa.eu/). In this case the
beneficiaries that have been estimated can be seen in the table 8.7;
Table 8.7 : beneficiaries and volumes of items estimated in each demand
points in function of damage and distribution of the population.
Demand point
Number of
Volume of E1
Volume of E2
beneficiaries
item to deliver
item to deliver
(
•
S
)
(
S
)
Carrefour (B)
225.000
450.000
900.000
Torgeau (C)
350.000
700.000
1.400.000
Pétionville (D)
275.000
550.000
1.100.000
Delmas (E)
150.000
300.000
600.000
Total
1.000.000
2.000.000
4.000.000
Different types of vehicle with all their own features (for example speed and
capacity). It is possible to choose a vehicle and study its features in the
distribution. In the next paragraph just one type of vehicle will be considered to
make an easier simulation but it is possible to consider all kinds of vehicle and
choose at first the cheapest one, as cost and as spent time. Here the vehicle we
have chosen is a truck, that is possible to use in a city, with 50,752
S
(cubic
meters) as capacity [8*2,44*2.6]. The time for the deliveries is 16 hours for
each day, as a double work turn. The vehicle features are taken by Man Trucks
and Bus website (www.mantruckandbus.it) but the trucks we use are taken
arbitrarily;
•
There are two kinds of item. The main difference between the items in the
distribution is the volume. The E1 item per person has 2 cubic decimeters per
person per day while the E2 item has 4 cubic decimeters per person. All these
91
data about the items are estimated by the real person needs. Indeed the RDA
(Recommended Daily Allowance) has been studied and from this the estimation
of the volume of the food the people need. The estimation of the E2 items has
been made studying the amount of the no-food needs per person and so
studying the volume of tents, blankets and mosquito nets people need.
8.7 Example of Output
As output it is possible to have two different matrixes, one for each kind of product, for
each vehicle we use. All the trucks are always full load and they can deliver or in just
one demand point or in more than one. In this simulation it is possible to note we have
to use 10 trucks to satisfy the demand.
92
Table 8.8 : Deliveries plan for the vehicle K1-1 during the t0 day.
Schedule of
deliveries
1
2
3
4
5
6
7
8
9
10
11
12
13
Schedule of
deliveries
1
2
3
4
5
6
7
8
9
10
11
12
13
Item E1 delivered by the Vehicle K1-1 in the t0 day
Delivered in Delivered in Delivered in Delivered in
B
C
D
E
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Item E2 delivered by the Vehicle K1-1 in the t0 day
Delivered in Delivered in Delivered in Delivered in
B
C
D
E
0
50752
0
0
0
50752
0
0
0
50752
0
0
0
50752
0
0
0
50752
0
0
0
50752
0
0
0
50752
0
0
0
50752
0
0
0
50752
0
0
0
50752
0
0
0
50752
0
0
0
50752
0
0
0
50752
0
0
Route
chosen
0
0
0
0
0
0
0
0
0
0
0
0
0
Route
chosen
2
2
2
2
2
2
2
2
2
2
2
2
2
93
Table 8.9 : Deliveries plan for the vehicle K1-2 during the t0 day.
Schedule of
deliveries
1
2
3
4
5
6
7
8
9
10
11
12
13
Schedule of
deliveries
1
2
3
4
5
6
7
8
9
10
11
12
13
94
Item E1 delivered by the Vehicle K1-2 in the t0 day
Delivered in Delivered in Delivered in Delivered in
B
C
D
E
0
0
0
0
0
50752
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
50752
0
0
0
0
0
0
0
0
0
0
0
50752
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
50752
0
0
Item E2 delivered by the Vehicle K1-2 in the t0 day
Delivered in Delivered in Delivered in Delivered in
B
C
D
E
0
50752
0
0
0
0
0
0
0
0
50752
0
0
50752
0
0
0
0
50752
0
0
0
0
0
0
0
50752
0
0
50752
0
0
0
0
0
0
0
0
50752
0
0
50752
0
0
0
0
50752
0
0
0
0
0
Route
chosen
0
2
0
0
0
2
0
0
2
0
0
0
2
Route
chosen
2
0
1
2
1
0
1
2
0
1
2
1
0
Table 8.10 : Deliveries plan for the vehicle K1-3 during the t0 day.
Schedule of
deliveries
1
2
3
4
5
6
7
8
9
10
11
12
Schedule of
deliveries
1
2
3
4
5
6
7
8
9
10
11
12
Item E1 delivered by the Vehicle K1-3 in the t0 day
Delivered in Delivered in Delivered in Delivered in
B
C
D
E
0
0
0
0
0
0
0
0
0
50752
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
50752
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Item E2 delivered by the Vehicle K1-3 in the t0 day
Delivered in Delivered in Delivered in Delivered in
B
C
D
E
0
0
50752
0
0
50752
0
0
0
0
0
0
0
0
50752
0
50752
0
0
0
0
50752
0
0
0
0
50752
0
0
0
0
0
50752
0
0
0
0
0
50752
0
50752
0
0
0
0
50752
0
0
Route
chosen
0
0
2
0
0
0
0
2
0
0
0
0
Route
chosen
1
2
0
1
8
2
1
0
8
1
8
2
95
Table 8.11 : Deliveries plan for the vehicle K1-4 during the t0 day.
Schedule of
deliveries
1
2
3
4
5
6
7
8
9
10
11
12
13
Schedule of
deliveries
1
2
3
4
5
6
7
8
9
10
11
12
13
96
Item E1 delivered by the Vehicle K1-4 in the t0 day
Delivered in Delivered in Delivered in Delivered in
B
C
D
E
0
0
0
0
0
50752
0
0
0
0
50752
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
50752
0
0
50752
0
0
0
0
0
0
0
0
0
0
0
0
50752
0
0
0
0
0
Item E2 delivered by the Vehicle K1-4 in the t0 day
Delivered in Delivered in Delivered in Delivered in
B
C
D
E
0
0
50752
0
0
0
0
0
0
0
0
0
50752
0
0
0
0
0
50752
0
0
50752
0
0
50752
0
0
0
0
0
0
0
0
0
0
0
50752
0
0
0
0
0
50752
0
0
0
0
0
0
50752
0
0
Route
chosen
0
2
1
0
0
0
0
1
2
0
0
1
0
Route
chosen
1
0
0
8
1
2
8
0
0
8
1
0
2
Table 8.12 : Deliveries plan for the vehicle K1-5 during the t0 day.
Schedule of
deliveries
1
2
3
4
5
6
7
8
9
10
11
12
Schedule of
deliveries
1
2
3
4
5
6
7
8
9
10
11
12
Item E1 delivered by the Vehicle K1-5 in the t0 day
Delivered in Delivered in Delivered in Delivered in
B
C
D
E
0
0
0
0
0
0
0
0
0
50752
0
0
0
0
0
0
0
0
50752
0
50752
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
50752
0
50752
0
0
0
0
50752
0
0
Item E2 delivered by the Vehicle K1-5 in the t0 day
Delivered in Delivered in Delivered in Delivered in
B
C
D
E
50752
0
0
0
0
0
50752
0
0
0
0
0
50752
0
0
0
0
0
0
0
0
0
0
0
0
0
50752
0
0
50752
0
0
50752
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Route
chosen
0
0
2
0
1
8
0
0
0
1
8
2
Route
chosen
8
1
0
8
0
0
1
2
8
0
0
0
97
Table 8.13 : Deliveries plan for the vehicle K1-6 during the t0 day.
Schedule of
deliveries
1
2
3
4
5
6
7
8
9
10
11
12
Schedule of
deliveries
1
2
3
4
5
6
7
8
9
10
11
12
98
Item E1 delivered by the Vehicle K1-6 in the t0 day
Delivered in Delivered in Delivered in Delivered in
B
C
D
E
0
0
0
0
0
0
0
0
0
0
0
0
50752
0
0
0
0
0
50752
0
0
0
0
0
0
0
0
0
0
50752
0
0
50752
0
0
0
0
0
0
0
0
0
0
0
0
0
50752
0
Item E2 delivered by the Vehicle K1-6 in the t0 day
Delivered in Delivered in Delivered in Delivered in
B
C
D
E
0
0
50752
0
50752
0
0
0
0
50752
0
0
0
0
0
0
0
0
0
0
0
0
50752
0
50752
0
0
0
0
0
0
0
0
0
0
0
50752
0
0
0
0
0
50752
0
0
0
0
0
Route
chosen
0
0
0
8
1
0
0
2
8
0
0
1
Route
chosen
1
8
2
0
0
1
8
0
0
8
1
0
Table 8.14 : Deliveries plan for the vehicle K1-7 during the t0 day.
Schedule of
deliveries
1
2
3
4
5
6
7
8
9
10
11
12
Schedule of
deliveries
1
2
3
4
5
6
7
8
9
10
11
12
Item E1 delivered by the Vehicle K1-7 in the t0 day
Delivered in Delivered in Delivered in Delivered in
B
C
D
E
0
0
0
0
50752
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
50752
0
0
0
0
50752
0
0
0
0
0
0
0
0
0
0
0
0
0
50752
0
0
0
0
0
0
0
Item E2 delivered by the Vehicle K1-7 in the t0 day
Delivered in Delivered in Delivered in Delivered in
B
C
D
E
0
50752
0
0
0
0
0
0
50752
0
0
0
0
0
50752
0
0
0
0
50752
0
0
0
0
0
0
0
0
0
0
0
50752
50752
0
0
0
0
50752
0
0
0
0
0
0
0
0
0
50752
Route
chosen
0
8
0
0
0
2
1
0
0
0
8
0
Route
chosen
2
0
8
1
4
0
0
4
8
2
0
4
99
Table 8.15 : Deliveries plan for the vehicle K1-8 during the t0 day.
Schedule of
deliveries
1
2
3
4
5
6
7
8
9
10
11
12
Schedule of
deliveries
1
2
3
4
5
6
7
8
9
10
11
12
100
Item E1 delivered by the Vehicle K1-8 in the t0 day
Delivered in Delivered in Delivered in Delivered in
B
C
D
E
0
0
0
0
0
0
0
0
0
0
50752
0
0
0
0
0
0
0
0
0
0
50752
0
0
50752
0
0
0
0
0
0
50752
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Item E2 delivered by the Vehicle K1-8 in the t0 day
Delivered in Delivered in Delivered in Delivered in
B
C
D
E
0
0
50752
0
0
0
0
50752
0
0
0
0
50752
0
0
0
0
0
0
50752
0
0
0
0
0
0
0
0
0
0
0
0
0
0
50752
0
0
0
0
50752
0
50752
0
0
50752
0
0
0
Route
chosen
0
0
1
0
0
2
8
4
0
0
0
0
Route
chosen
1
4
0
8
4
0
0
0
1
4
2
0
Table 8.16 : Deliveries plan for the vehicle K1-9 during the t0 day.
Schedule of
deliveries
1
2
3
4
5
6
7
8
9
10
11
12
Schedule of
deliveries
1
2
3
4
5
6
7
8
9
10
11
12
Item E1 delivered by the Vehicle K1-9 in the t0 day
Delivered in Delivered in Delivered in Delivered in
B
C
D
E
0
0
50752
0
0
0
0
50752
0
0
0
0
0
0
50752
0
50752
0
0
0
0
0
0
0
0
0
0
50752
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
50752
0
40224
0
10528
Item E2 delivered by the Vehicle K1-9 in the t0 day
Delivered in Delivered in Delivered in Delivered in
B
C
D
E
0
0
0
0
0
0
0
0
0
0
0
50752
0
0
0
0
0
0
0
0
0
0
0
50752
0
0
0
0
0
0
50752
0
0
0
0
50752
50752
0
0
0
0
0
0
0
0
0
0
0
Route
chosen
1
4
0
1
8
0
4
0
0
0
4
6
Route
chosen
0
0
4
0
0
4
0
1
4
8
0
0
101
Table 8.17: Deliveries plan for the vehicle K1-10 during the t0 day.
Schedule of
deliveries
1
2
3
4
5
6
7
8
9
Schedule of
deliveries
1
2
3
4
5
6
7
8
9
Item E1 delivered by the Vehicle K1-10 in the t0 day
Delivered in Delivered in Delivered in Delivered in
B
C
D
E
0
0
0
0
0
0
0
0
43984
0
6768
0
0
0
0
50752
0
0
30637
20115
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
15597
Item E2 delivered by the Vehicle K1-10 in the t0 day
Delivered in Delivered in Delivered in Delivered in
B
C
D
E
0
0
0
50752
0
29096
0
21656
0
0
0
0
0
0
0
0
0
0
0
0
16550
0
34202
0
0
0
0
50752
20666
0
0
20072
0
0
0
0
Route
chosen
0
0
10
4
5
0
0
0
4
Route
chosen
4
6
0
0
0
10
4
12
0
8.8 Discussion of results
In the tables it is possible to see the distribution program for each vehicle in a day
during the disaster phase we have studied. In this situation we need 10 vehicles to
complete the distribution in this area to satisfy the demand of 1’000’000 people
(6’000’000
S
of materials).
It is important to see that for the first vehicles the distribution routes are the direct ones
while the next vehicles touch more nodes to complete all the needs of the beneficiaries.
So the first trucks deliver just to one deliver point each time, at first each truck delivers
all the 50,752 cubic meters to a demand point. This is important because the cheapest
routes are the direct ones and the multi-nodes routes are used just when the most
important objective is to complete the deliveries in places that don’t need a complete
capacity delivery.
102
Another important note is that this is not a real distribution plan because it doesn’t
consider some real data but it could be a good step to understand how to optimize the
deliveries and which information are important to center this critical objective.
103
104
9 Conclusions
“This thesis aims to present the most basic concepts of humanitarian logistics: the state of art
upon humanitarian logistics and last mile distribution in a humanitarian aids operation, taking
Haitian case as example. The logistics principles that will be described here have multi-sectoral
applications, not only in emergency situations, but also in the day-to-day operations that must
be a part of disaster prevention and preparedness”. (Peretti, 2011)
The idea of the thesis has been to show the state of art upon humanitarian logistics and
last mile distribution in a humanitarian relief operation, taking Haitian case as example.
This has been done. All problems and features have been studied and many critic points
have been found. All problems that have been found are about the critical environment
in which humanitarian logistic usually works. Indeed all the uncertainties and the
environment features, we can find in this field, make it a hard logistic operations camp.
If we consider the Haitian case, the main problem that has been found is that in Haiti
earthquake, and so usually in post-disaster periods, there was a high uncertainty of the
data. For example for the first weeks after the seism it was difficult to have information
about people, damages and infrastructures. This happened when the data changes
quickly. Day by day, with always more information, the situation in the country
becomes more clear.
As we have seen in the chapter 5, figure 5.1, the phase where our formulation will be
used in the humanitarian operations lifecycle is showed, and by this figure it is possible
to see that in this phase there are not a lot of information and so it is impossible to make
a real standardization. For this reason the possibility to find a complete optimization of
the distribution is difficult. It could be possible to find a day by day heuristic
optimization to maximize all the supplies minimizing the time for each route.
Another important discussion point would be about in which situation this research
could be used. It is important to note that all the features that have been studied are the
typical humanitarian operations features. So it is possible to say that the idea of this
script can be applied even in others post-disaster situations.
105
At the end the program showed is just an example studied to find the optimization in the
distribution of the humanitarian deliveries. It is important to note this is not a realistic
distribution plan because it doesn’t consider some real data but it could be a good step
to understand how optimize the deliveries and which information are important to focus
this critical objective
106
10 Future discussions
In this script many critic features of humanitarian logistics have been studied, for
example cluster approach and last mile distribution.
To further the researches would be some deep studies about all these features. An
example can be a research about all the information that we need to know the
distribution of the population and of the damage. This is fundamental for all the next
steps of the study. All of these research have to be planned and made in the
preparedness phase. In this phase indeed these kinds of research can develop some tools
that could be used subsequently, after the natural disaster.
A second point that can be studied is the study of optimization of each phase. Indeed in
this paper the optimization has been studied just in the first phase of the support, with
some particular objectives and drivers. So all the others phases in the humanitarian
lifecycle can be examined with their own drivers and objectives.
Furthermore for each phase can be found an algorithmic and a program for the
optimization point.
107
108
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113
114
Attachment 1
Program in Matlab
%DATA
C1=[c11;…;c1x;…;c1n];
C2=[c21;…;c2x;…;c2n];
%costs vehicle 1 and vehicle 2
% import
T1=[t11;…;t1i;…;t1n];
T2=[t21;…;t2i;…;t2n];
% times vehicle 1 and vehicle 2
% import
for each route
for each route
Q1=q1;
Q2=q2;
% capacity of vehicles 1 and 2
P = [0, p1b, p1c, p1d, p1e];
P2 = [0, p2b, p2c, p2d, p2e];
% cost penalty for unsatisfied demand for each items in each distribution
point
S=[0 0 0 0 0 ; 0 0 0 0 1 ; 0 0 0 1 0 ; … ; 1 1 1 1 1];
% S is 1 if the route touches the demand point otherwise it is 0
% import
Qdd_iniz = [0, Qdd1b, Qdd1c, Qdd1d,
Qdd
= [0, Qdd1b, Qdd1c, Qdd1d,
Qdd2_iniz = [0, Qdd2b, Qdd2c, Qdd2d,
Qdd2
= [0, Qdd2b, Qdd2c, Qdd2d,
%Qdd= quantity of matirial has to be
Qdd1e];
Qdd1e];
Qdd2e];
Qdd2e];
delivered
%PROGRAM
n = length(T1);
t = length(P);
numero_giri = 0;
Time_total = 0;
giorno = 0;
matrice_output = zeros(1,6);
matrice_output2 = zeros(1,6);
while Time_total <= 1 && (sum(Qdd) > 0 || sum(Qdd2) > 0)
Coeff_maxperdita = P .* Qdd;
c1 = max(Coeff_maxperdita);
Coeff_maxperdita2 = P2 .* Qdd2;
c2 = max(Coeff_maxperdita2);
if c1 > c2
% DEMAND POINT/S CHOICE (Item E1 max priority)
Coeff_maxperdita = P .* Qdd;
[c,d] = max(Coeff_maxperdita);
sceltadistribuzione_rotta = zeros(1,t);
sceltadistribuzione_rotta(d) = 1;
sceltadistribuzione_ok = sceltadistribuzione_rotta*Q1*P(d);
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Coeff_maxperdita = Coeff_maxperdita - sceltadistribuzione_ok;
if Qdd(d) >= Q1
qdr = sceltadistribuzione_rotta * Q1;
Qdd = Qdd - qdr;
else
distribuito1 = Qdd(d);
sceltadistribuzione_rotta = ones(1,t);
sceltadistribuzione_rotta(d) = 0;
Coeff_maxperdita = P .* Qdd .* sceltadistribuzione_rotta;
[c,e] = max(Coeff_maxperdita);
sceltadistribuzione_rotta = zeros(1,t);
sceltadistribuzione_rotta(d) = 1;
sceltadistribuzione_rotta(e) = 1;
sceltadistribuzione_ok = sceltadistribuzione_rotta;
sceltadistribuzione_ok(d) = distribuito1 * P(d);
if Qdd(e) >= Q1 - distribuito1
sceltadistribuzione_ok(e) = Q1 - distribuito1;
qdr = sceltadistribuzione_rotta;
qdr(d) = distribuito1;
qdr(e) = Q1 - distribuito1;
Qdd = Qdd - qdr;
else
distribuito2 = Qdd(e);
sceltadistribuzione_rotta = ones(1,t);
sceltadistribuzione_rotta(d) = 0;
sceltadistribuzione_rotta(e) = 0;
Coeff_maxperdita = P .* Qdd .* sceltadistribuzione_rotta;
[c,f] = max(Coeff_maxperdita);
sceltadistribuzione_rotta = zeros(1,t);
sceltadistribuzione_rotta(d) = 1;
sceltadistribuzione_rotta(e) = 1;
sceltadistribuzione_rotta(f) = 1;
sceltadistribuzione_ok = sceltadistribuzione_rotta;
sceltadistribuzione_ok(d) = distribuito1 * P(d);
sceltadistribuzione_ok(e) = distribuito2 * P(e);
if Qdd(f) >= Q1 - distribuito1 - distribuito2
sceltadistribuzione_ok(f) = Q1 - distribuito1 - distribuito2;
qdr = sceltadistribuzione_rotta;
qdr(d) = distribuito1;
qdr(e) = distribuito2;
qdr(f) = Q1 - distribuito1 - distribuito2;
Qdd = Qdd - qdr;
else
distribuito3 = Qdd(f);
sceltadistribuzione_rotta = ones(1,t);
sceltadistribuzione_rotta(d) = 0;
sceltadistribuzione_rotta(e) = 0;
sceltadistribuzione_rotta(f) = 0;
116
Coeff_maxperdita = P .* Qdd .* sceltadistribuzione_rotta;
[c,g] = max(Coeff_maxperdita);
sceltadistribuzione_rotta = zeros(1,t);
sceltadistribuzione_rotta(d) = 1;
sceltadistribuzione_rotta(e) = 1;
sceltadistribuzione_rotta(f) = 1;
sceltadistribuzione_rotta(g) = 1;
sceltadistribuzione_ok = sceltadistribuzione_rotta;
sceltadistribuzione_ok(d) = distribuito1 * P(d);
sceltadistribuzione_ok(e) = distribuito2 * P(e);
sceltadistribuzione_ok(f) = distribuito3 * P(f);
if Qdd(g) >= Q1 - distribuito3 - distribuito2 - distribuito1
sceltadistribuzione_ok(g) = Q1 - distribuito1 distribuito2 - distribuito3;
qdr = sceltadistribuzione_rotta;
qdr(d) = distribuito1;
qdr(e) = distribuito2;
qdr(f) = distribuito3;
qdr(g) = Q1 - distribuito3 - distribuito2 - distribuito1;
Qdd = Qdd - qdr;
else
distribuito4 = Qdd(g);
sceltadistribuzione_ok(g) = Q1 - distribuito1 distribuito2 - distribuito3;
qdr = sceltadistribuzione_rotta;
qdr(d) = distribuito1;
qdr(e) = distribuito2;
qdr(f) = distribuito3;
qdr(g) = distribuito4;
Qdd = Qdd - qdr;
end
end
end
end
else
% DEMAND POINT/S CHOICE (Item E2 max priority)
Coeff_maxperdita = P2 .* Qdd2;
[c,d] = max(Coeff_maxperdita);
sceltadistribuzione_rotta = zeros(1,t);
sceltadistribuzione_rotta(d) = 1;
sceltadistribuzione_ok = sceltadistribuzione_rotta*Q1*P2(d);
Coeff_maxperdita = Coeff_maxperdita - sceltadistribuzione_ok;
if Qdd2(d) >= Q1
qdr = sceltadistribuzione_rotta * Q1;
Qdd2 = Qdd2 - qdr;
else
distribuito1 = Qdd2(d);
sceltadistribuzione_rotta = ones(1,t);
117
sceltadistribuzione_rotta(d) = 0;
Coeff_maxperdita = P2 .* Qdd2 .* sceltadistribuzione_rotta;
[c,e] = max(Coeff_maxperdita);
sceltadistribuzione_rotta = zeros(1,t);
sceltadistribuzione_rotta(d) = 1;
sceltadistribuzione_rotta(e) = 1;
sceltadistribuzione_ok = sceltadistribuzione_rotta;
sceltadistribuzione_ok(d) = distribuito1 * P2(d);
if Qdd2(e) >= Q1 - distribuito1
sceltadistribuzione_ok(e) = Q1 - distribuito1;
qdr = sceltadistribuzione_rotta;
qdr(d) = distribuito1;
qdr(e) = Q1 - distribuito1;
Qdd2 = Qdd2 - qdr;
else
distribuito2 = Qdd2(e);
sceltadistribuzione_rotta = ones(1,t);
sceltadistribuzione_rotta(d) = 0;
sceltadistribuzione_rotta(e) = 0;
Coeff_maxperdita = P2 .* Qdd2 .* sceltadistribuzione_rotta;
[c,f] = max(Coeff_maxperdita);
sceltadistribuzione_rotta = zeros(1,t);
sceltadistribuzione_rotta(d) = 1;
sceltadistribuzione_rotta(e) = 1;
sceltadistribuzione_rotta(f) = 1;
sceltadistribuzione_ok = sceltadistribuzione_rotta;
sceltadistribuzione_ok(d) = distribuito1 * P2(d);
sceltadistribuzione_ok(e) = distribuito2 * P2(e);
if Qdd2(f) >= Q1 - distribuito1 - distribuito2
sceltadistribuzione_ok(f) = Q1 - distribuito1 - distribuito2;
qdr = sceltadistribuzione_rotta;
qdr(d) = distribuito1;
qdr(e) = distribuito2;
qdr(f) = Q1 - distribuito1 - distribuito2;
Qdd2 = Qdd2 - qdr;
else
distribuito3 = Qdd2(f);
sceltadistribuzione_rotta = ones(1,t);
sceltadistribuzione_rotta(d) = 0;
sceltadistribuzione_rotta(e) = 0;
sceltadistribuzione_rotta(f) = 0;
Coeff_maxperdita = P2 .* Qdd2 .* sceltadistribuzione_rotta;
[c,g] = max(Coeff_maxperdita);
sceltadistribuzione_rotta = zeros(1,t);
sceltadistribuzione_rotta(d) = 1;
sceltadistribuzione_rotta(e) = 1;
sceltadistribuzione_rotta(f) = 1;
sceltadistribuzione_rotta(g) = 1;
sceltadistribuzione_ok = sceltadistribuzione_rotta;
sceltadistribuzione_ok(d) = distribuito1 * P2(d);
sceltadistribuzione_ok(e) = distribuito2 * P2(e);
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sceltadistribuzione_ok(f) = distribuito3 * P2(f);
if Qdd2(g) >= Q1 - distribuito3 - distribuito2 - distribuito1
sceltadistribuzione_ok(g) = Q1 - distribuito1 distribuito2 - distribuito3;
qdr = sceltadistribuzione_rotta;
qdr(d) = distribuito1;
qdr(e) = distribuito2;
qdr(f) = distribuito3;
qdr(g) = Q1 - distribuito3 - distribuito2 - distribuito1;
Qdd2 = Qdd2 - qdr;
else
distribuito4 = Qdd2(g);
sceltadistribuzione_ok(g) = Q1 - distribuito1 distribuito2 - distribuito3;
qdr = sceltadistribuzione_rotta;
qdr(d) = distribuito1; % + Q1 - distribuito1 distribuito2 - distribuito3 - distribuito4;
qdr(e) = distribuito2;
qdr(f) = distribuito3;
qdr(g) = distribuito4;
Qdd2 = Qdd2 - qdr;
end
end
end
end
end
% ROUTE CHOICE (most convenient, less time)
for i=1:n
if sum (sceltadistribuzione_rotta) == 1
h = find(sceltadistribuzione_rotta == 1);
if S(i,h) == 1
funzione(i) = C1(i) + sum(Coeff_maxperdita);
else
funzione(i) = Inf;
end
elseif sum (sceltadistribuzione_rotta) == 2
h = find(sceltadistribuzione_rotta == 1);
x = h(1);
y = h(2);
if S(i,x) == 1 && S(i,y) == 1
funzione(i) = C1(i) + sum(Coeff_maxperdita);
else
119
funzione(i) = Inf;
end
elseif sum (sceltadistribuzione_rotta) == 3
h
x
y
z
=
=
=
=
find(sceltadistribuzione_rotta == 1);
h(1);
h(2);
h(3);
if S(i,x) == 1 && S(i,y) == 1 && S(i,z) == 1
funzione(i) = C1(i) + sum(Coeff_maxperdita);
else
funzione(i) = Inf;
end
else
h
x
y
z
w
=
=
=
=
=
find(sceltadistribuzione_rotta == 1);
h(1);
h(2);
h(3);
h(4);
if S(i,x) == 1 && S(i,y) == 1 && S(i,z) == 1 && S(i,w) == 1
funzione(i) = C1(i) + sum(Coeff_maxperdita);
else
funzione(i) = Inf;
end
end
[a,b] = min(funzione);
% a is the minimum value, b is the position in the array
end
% CHOICE OF THE LAST DAY TOUR
Time_total = Time_total + T1(b);
if Time_total - T1(b)/2 <= 1
numero_giri = numero_giri + 1;
if c1 > c2
matrice_output(numero_giri,:) = [qdr, b];
else
matrice_output2(numero_giri,:) = [qdr, b];
end
else
break
end
end
120
A = sum (matrice_output);
B = sum (matrice_output2);
A(6) = NaN;
B(6) = NaN;
non_distribuito1 = [Qdd_iniz,0] - A;
non_distribuito2 = [Qdd2_iniz,0] - B;
121