M A R M A R A U N I V E R S I T Y

I N S T I T U T E F O R G R A D U A T E S T U D I E S

I N P U R E A N D A P P L I E D S C I E N C E S

A V E H I C L E A L L O C A T I O N M O D E L F O R

P O S T - D I S A S T E R D E B R I S R E M O V A L

O P E R A T I O N S

LAMİA GÜLNUR KASAP

MASTER THESIS

Department of Indust r ia l Enginee ring

Thesis Supervisor

Prof . Dr . Özalp VAYVAY

Thesis CO- Supervisor

Assoc. Prof. Dr. Gülfem TUZKAYA

ISTANBUL, 2016

M A R M A R A U N I V E R S I T Y

I N S T I T U T E F O R G R A D U A T E S T U D I E S

I N P U R E A N D A P P L I E D S C I E N C E S

A V E H I C L E A L L O C A T I O N M O D E L F O R

P O S T - D I S A S T E R D E B R I S R E M O V A L

O P E R A T I O N S

LAMİA GÜLNUR KASAP

(524413015)

MASTER THESIS

Department of Indust r ia l Enginee ring

Thesis Supervisor

Prof . Dr . Özalp VAYVAY

Thesis CO- Supervisor

Assoc. Prof. Dr. Gülfem TUZKAYA

ISTANBUL, 201 6

i

ACKNOWLEDGEMENT

I would like to present my sincere gratitude to my supervisors, Prof. Dr. Özalp

VAYVAY and especially Associate Prof. Gülfem TUZKAYA for their

stimulating suggestions, valuable contributions, encouraging guidance, support,

endless help and patience through research for and writing of this thesis.

I am grateful to Prof. Talip Alp for accepting to read this thesis and for his

valuable suggestions and precious support.

I would like to give my heartily appreciation to my family; my mother Nevin

KASAP, my father Asst. Prof. Dr. Hüseyin KASAP and my brother Caner Yavuz

KASAP for their persistent help, encouragement, patience, and it will be

impossible to come this day without their support.

I want to thank my lovely friends for their precious support. I really appreciate to

all of you for your great companionship, motivation and advices.

ii

CONTENTS

ACKNOWLEDGEMENT ................................................................................................. i

CONTENTS ..................................................................................................................... ii

ÖZET ............................................................................................................................. iv

ABSTRACT ..................................................................................................................... v

SYMBOLS ..................................................................................................................... vii

ABBREVIATIONS .......................................................................................................... x

LIST OF FIGURES ......................................................................................................... xi

LIST OF TABLES.......................................................................................................... xii

1. INTRODUCTION ........................................................................................................ 1

2. LITERATURE REVIEW ..................................................................................... 3

2.1. Disaster Operation Management and Humanitarian Logistics ............................. 4

2.2. Disaster Management / Humanitarian Logistics Categories................................. 7

2.2.1. Pre-disaster categories ............................................................................................ 9

2.2.1.1. The preparation phase .......................................................................................... 9

2.2.1.2. The mitigation phase ......................................................................................... 10

2.2.2. Post-disaster categories......................................................................................... 10

2.2.2.1. The response phase ............................................................................................ 11

2.2.2.2. The recovery phase ............................................................................................ 11

2.3. Applications in Turkey: Previous Studies .......................................................... 12

2.4. Debris Removal Operation ................................................................................. 12

2.4.1. Debris recycling operation.................................................................................... 16

3. PROBLEM DEFINITION AND SOLUTION APPROACH ............................. 17

3.1. Solution Approach .............................................................................................. 18

3.2. General Structure and Formulation of Model ....................................................... 1

3.3. Modeling Challenges ............................................................................................ 9

3.4. Estimation of Input Data ..................................................................................... 10

3.4.1. Test data set...................................................................................................... 15

3.5. Results and Analysis ........................................................................................... 16

4. CONCLUSIONS ........................................................................................................ 19

REFERENCES ............................................................................................................... 20

iii

APPENDIX ................................................................................................................................25

iv

ÖZET

AFET SONRASI ENKAZ KALDIRMA OPERASYONLARI İÇİN

BİR ARAÇ ATAMASI MODELİ

Afetin insan, doğa, ekonomi ve sosyal yapı üzerindeki etkilerini azaltmak için

hazırlanan afet operasyon yönetimi planları çok ciddi bir öneme sahiptir. Afet

operasyon çalışmaları; müdahale ve iyileşme evrelerindeki çalışmaların sürelerini

kısaltmak, operasyon maliyetlerini azaltmak ve daha koordineli olan yeni teknikler ile

birlikte yeni planlar oluşturmak açısından özellikle son zamanlarda önemini gittikçe

arttırmıştır. Enkaz Kaldırma Operasyonları da afet operasyon çalışmalarının bir konusu

olarak bu bağlamda önem kazanmıştır.

Enkaz operasyon planları depremden hemen sonra başlar. Diğer yandan, enkaz kaldırma

operasyonları, arama kurtarma çalışmaları tamamlandıktan sonra başlar. Enkaz kaldırma

operasyonları afet sonrası kategorisi ve iyileştirme evresi çalışmalarından biridir. Afet

operasyonları yönetiminde, enkazın afetzedeler üzerindeki negatif etkisi ve çevresel

etkilerinden dolayı olabildiğince çabuk kaldırılması çok önemlidir. Enkazın toplanması

işleminden sonra doğru bir şekilde bertaraf edilmemesi ya da geri dönüşümün

yapılmaması durumunda ileriye yönelik yer altı kaynaklarına, toprak ve havaya ciddi

zararları bulunmaktadır.

Bu çalışmada, Sakarya ili afet sonrası enkaz temizleme problemi için araç

lokasyonlarından depremden etkilenen bölgelere ve geçici depolama bölgelerine araç

atama modeli önerilmiştir. Ayrıca modelde enkazın geri dönüşüm işleminin

yapılabileceği geçici depolama alanlarının açma kapama kararları da bulunmaktadır.

Önerilen matematiksel model (i) enkaz kaldırma operasyonlarının toplam maliyetini, (ii)

enkazın etkilenen insanlar üzerindeki negatif etkisini ve (iii) atanan araçların emisyon

indeksini en küçüklemeyi hedefleyen üç amaçlı bir modeldir. Lingo optimizasyon

programı ile çözdürülen çok amaçlı modelin amaç fonksiyonlarını birleştirerek çözmek

için Lineer Fiziksel Programlama tekniğinden yararlanılmıştır. Çözüm sonucunda tüm

amaç fonksiyonları ideal aralıkta bulunmuştur.

Anahtar Kelimeler: Enkaz Kaldırma, Afet Operasyon Yönetimi, İnsani Yardım

Lojistiği, Araç Atama, Lineer Fiziksel Programlama

v

ABSTRACT

A VEHICLE ALLOCATION MODEL FOR POST-DISASTER

DEBRIS REMOVAL OPERATIONS

Disaster operation plan is of vital importance in reducing the effect of disaster on human

beings, natural environment, national economy and social structures. The new

technologies developed recently which, have shortened the time taken for the tasks

undertaken in the stages of intervention and improvement, and have resulted in

minimizing operational costs, and bringing in new techniques of coordination have

rendered the importance of the disaster operation studies has increased steadily,

especially in recent times. Debris Removal Operations as a subject of the disaster

operation studies has become more important in this context.

Disaster Operation Plans start immediately after disaster occurs. On the other hand,

debris removal operations start after search and rescue operations are being completed.

They are after-disaster and recovery phase activities. The debris need to be gathered as

quickly as possible in order to ease the negative impact of the catastrophe on the

affected people and nature. If Debris is not recycled or disposed off promptly and

properly after the collection process, may cause environmental pollution (water

resources, soil and air).

In this study, in order to address the debris removal problem, a mathematical model has

been proposed to assign the debris removal vehicles from locations of the vehicles to

the affected districts of Sakarya and predetermined temporary storage areas. The model

is also capable of making rational decisions for opening temporary storage areas,

designed for recycling the debris. The mathematical model has employed in this study

three objectives: (i) minimization of operational costs, (ii) mitigation of the negative

impact of the disaster on the affected people, and (iii) minimization of the emission

index of the assigned vehicles. Linear Physical Programming technique is used to

combine the three objective functions of the multi objective model and Lingo

optimization software is used to run the proposed model. As a result of the proposed

model’s solution, all objective functions are found in ideal range.

vi

Key Words: Debris Removal, Disaster Operation Management, Humanitarian Logistics,

Vehicle Allocation, Linear Physical Programming

vii

SYMBOLS

i : Disaster-affected area

j : Vehicle location

k : Temporary storage area

t : Time period

p : Process type

d : Disposal area

e : Emission index

𝑿𝟏𝒋𝒊𝒆𝒕 : The amount of excavator with e emission index is assigned from vehicle

location j to disaster affected area i at time period t

𝑿𝟐𝒋𝒊𝒆𝒕 : The amount of loader with e emission index is assigned from vehicle location j

to disaster affected area i at time period t

𝑿𝟑𝒋𝒊𝒆𝒕 : The amount of truck with e emission index is assigned from vehicle location j

to disaster affected area i at time period t

𝑿𝟒𝒋𝒌𝒆𝒕 : The amount of loader with e emission index is assigned from vehicle

location j to temporary storage area k at time period t

𝑿𝟓𝒋𝒌𝒆𝒕 : The amount of truck with e emission index is assigned from vehicle

location j to temporary storage area k at time period t

𝒁𝒊𝒌𝒆𝒕 : The amount of truck with e emission index transport the debris from affected

area i to temporary storage area k at time period t

𝒁𝟏𝒌𝒅𝒆𝒕 : The amount of truck with e emission index transport the debris from

temporary storage area k to disposal area d at time period t

𝝁𝒌 : Decision of opening temporary storage area k (0;1)

𝑸𝒌𝒕 : The amount of debris in the temporary storage area k at time period t

𝒈𝒅𝒎𝒌𝒕 : The amount of recycled debris in temporary storage area k at time period t

𝒈𝒅𝒑𝒎𝒌𝒑𝒕 : The amount of recycled p type debris in temporary storage area k at time

period t

viii

𝒂𝒊𝒕 : The amount of debris remaining in the affected area i at time period t

𝒂𝟏𝒊𝒕 : The destruction amount remaining in the affected area i at time period t

𝒕𝒎𝒊𝒌𝒊𝒌𝒕: Debris transported from to disaster affected area i to temporary storage area k

at time period t

𝒕𝒎𝒌𝒌𝒅𝒕: Debris transported from to temporary storage area k to disposal area d at time

period t

𝒎𝒋𝒊 : The cost of assigning process of vehicles from vehicle location k to affected

area i

𝒎𝟏𝒋𝒌 : The cost of assigning process of vehicles from vehicle location k to temporary

storage area k

𝒇𝟏𝒋𝒆 : The amount of excavator with e emission index at vehicle location j

𝒇𝟐𝒋𝒆 : The amount of loader with e emission index at vehicle location j

𝒇𝟑𝒋𝒆 : The amount of truck with e emission index at vehicle location j

𝒄𝟏 : Capacity of each excavator in the vehicle location

𝒄𝟐 : Capacity of each loader in the vehicle location

𝒄𝟑 : Capacity of each truck in the vehicle location

𝒄𝒌𝒌 : Capacity of temporary storage area k

𝒅𝟏𝒌𝒅 : Distances between temporary storage area k and disposal area d

𝒅𝟐𝒊𝒌 : Distances between disaster affected area i and temporary storage area k

𝒅𝟑𝒋𝒊 : Distances between vehicle location j and disaster affected area i

𝒅𝟒𝒋𝒌 : Distances between vehicle location j and temporary storage area k

𝒂𝒂𝒊 : The estimated amount of debris at disaster affected area i

𝒂𝒂𝟏𝒊 : The estimated amount to be demolition at disaster affected area i

𝜹𝒌𝒑 : Debris ratio for process p which can be done at temporary storage area k

𝒈𝒑 : income derived from process p

ix

𝝋𝒌 : Cost of opening / closing temporary storage area k

𝝋𝟏𝒌 : Recycling processing cost at temporary storage area k

𝒏𝟏 : Demolishing costs

𝒏𝟐 : Loading / unloading costs

𝒏𝟑 : Transportation costs

𝒏𝒆𝒊 : Population ratio of i location

𝒏𝒊𝒓𝒂𝒕𝒆𝒕 : Negative impact rate on i location at t time

𝒆𝒅𝒆 : Emission penalty of e emission index

B : Fuel costs

Rje : Vehicle usage cost has to e emission index in j vehicle location

x

ABBREVIATIONS

OR : Operation Research

AFAD : The Disaster and Emergency Management Authority

İSTAÇ : İstanbul Büyükşehir Belediyesi Çevre Koruma ve Atık Maddeleri

Değerlendirme Sanayi ve Ticaret A.Ş.

GIS : Geographical Information System

TUİK : Turkish Statistical Institute

TSA : Temporary storage areas

OF : Objective Function

LPP : Linear Physical Programming

DAS : Disaster Affected Site

xi

LIST OF FIGURES

Figure 1.1. Disasters’ types and inputs/outputs (modified from Can, 2005) ................... 3

Figure 2.1.1. Challenges of humanitarian logistics (Kova´cs and Spens, 2009) ............. 5

Figure 2.1.2 Stakeholders in Humanitarian Logistics (Wisetjindawat et al., 2014) ........ 6

Figure 2.1.3. Phases of relief efforts (Wisetjindawat et al., 2014) .................................. 6

Figure 2.2.1. Categories and Phases of Disaster / Humanitarian Logistics ..................... 7

Figure 2.4.1 An image of an uncontrolled disposal of debris (T.C. Tekirdağ Valiliği İl

Çevre ve Orman Müdürlüğü) ......................................................................................... 13

Figure 2.4.2 Categories and Phases of Debris Management (Modified from Şahin,

2013) ............................................................................................................................... 15

Figure 2.4.1.1 Basic approach of Debris recycling operation (Öztürk, 2005) ............... 16

Figure 2.4.1.2 Combined approach of Debris recycling operation (Öztürk, 2005) ....... 16

Figure 2.4.1.3 Approach of Debris recycling operation with using recycling machines

(Öztürk, 2005) ................................................................................................................ 16

Figure 2.4.1.4 Recycling machines are used at facility in Istanbul (İSTAÇ) ................ 17

Figure 3.1.1. Class functions for linear physical programming (Maria et al., 2003) .... 20

Figure 3.2.1. Problem definition ...................................................................................... 2

Figure 3.4.1. DASs , TSAs, location of vehicles, disposal sites and their relations ...... 12

Figure 3.4.2. Penalty of e emission index ...................................................................... 14

Figure 3.5.1. Lingo screen of the proposed model by using input data ......................... 17

xii

LIST OF TABLES

Table 2.2.1. Pre-disaster and Post-disaster Activities of Humanitarian Logistics

(Gösling and Geldermann, 2014) ..................................................................................... 8

Table 2.2.1-continue Pre-disaster and Post-disaster Activities of Humanitarian Logistics

(Gösling and Geldermann, 2014) ..................................................................................... 9

Table 2.2.1.1.1. Topics of Pre-Disaster (Güler, 2005) ................................................... 10

Table 3.4.1. Weights of objective functions ................................................................... 10

Table 3.4.2. Quantity of demolished and heavily damaged buildings ........................... 11

Table 3.4.3. Capacity of vehicles and cost of work ........................................................ 13

Table 3.4.4. Quantity of excavator, loader and truck at location of vehicle j ................ 13

Table 3.4.5. The sales prices of recycled materials ........................................................ 14

Table 3.4.6. The recycling rates of debris ...................................................................... 15

Table 3.4.1.1. The amount of debris, the numbers of demolished buildings and

population rates............................................................................................................... 15

Table 3.4.1.2. The distances between locations ............................................................. 16

Table 3.4.1.3. Test set’s quantity of excavator, loader and truck at location of vehicle j

........................................................................................................................................ 16

Table 3.5.1. The preference ranges of the case study ..................................................... 17

Table 3.5.3. Normalized weight deviations of objectives .............................................. 17

Table 3.5.4. The numerical analysis ............................................................................... 18

Table 3.5.5. Quantity of assigned excavator, loader and truck from location of vehicle j

to DAS i and TSA k ........................................................................................................ 18

1

1. INTRODUCTION

Disaster is a sudden and unexpected event which has a phenomenally negative impact

on people, environment and economy due to the massive destruction, caused in terms of

loss of life and property. Hence, significance of the work done in this regard can hardly

be emphasized. Disaster Operation Management activities are used to reduce the

detrimental effects of the disaster on people, material, social welfare, wealth and human

casualties. Such studies focus on identifying locations of suitable disaster logistics

centers and evacuation facilities, the position of responders and facility location

assignments between facilities, preparing strategic plans for search and rescue

operation, transforming the victims from disaster affected area to shelters, emergency

relief operations, etc. In order to coordinate these activities, disaster operations deal

with three categories of events classified as “pre-disaster”, “during disaster” and “post

disaster”, developments divided into four phases named “mitigation”, “preparation”,

“response” and “recovery”.

Of the disaster operation management activities, the post-disaster activities have high

uncertainty since it is difficult to establish the exact time and location of the disaster, the

size of the destruction and uncertain demand. Operation Research (OR) methods are the

most common decision analysis and quantitative methods the used to eliminate. OR

methods offer a systematic approach to find the optimal or nearly optimal solution and

scenario analysis might be used to eliminate the uncertainties.

Through implementing well-designed post-disaster plans, both ensuring the

coordination between activities and using time efficiently, losses would be minimized.

Efficient time management and scheduling of activities are important for all post-

disaster plans and alleviation of the negative impact on the victims. From this point of

view, post-disaster studies may have multiple objectives. Hence, an effective solution

for the purpose of identifying priorities can be provided by using the normalization

methods.

It has been argued in recent years that urban transformation of civil structures, i.e.

reconstruction of very old buildings, possibly affected by previous earthquakes, with

2

diligent planning can contribute significantly to the reduction of debris and negative

impact on the victims with using time effectively.

In this thesis, the debris removal problem was focused in order to use time efficiently

and reduce the negative impacts on people and environment for the case of the

earthquake in Sakarya. As a result of the existing literature investigation, studies on the

collection and disposal of debris have been found, on the other hand the study on the

recycling process of the debris has not been found. In the most of the studies, problem is

investigated as arc routing problem.

As in all phases on life, in case of disaster, limited resources coupled with huge and

fatal emergencies require the available resources to be used efficiently in a highly

number of coordinated manner. Whitin this context assignment of limited number of

vehicles from vehicle locations to the disaster affected areas and temporary storages

assume vitally importance. Also if the debris is not collected with appropriate and

accurate methods, the land, underground water and the surrounding environment may

become contaminated. Improperly stacked debris can cause various hazards including

environmental pollutions such as land, air quality, and water system. Additionally, pose

serious danger citizens who attempt to recover recyclable scrap without any permission.

Considering these situations, a mathematical method is proposed in order to spend

efficiently time, reduce the negative impact on victims and on the environment

(emission index). Linear Physical Programming technique is used to combine the multi

objectives function. The data used in the model is created based on Sakarya district of

Turkey. On the other hand, the smallest input data are used to solve the proposed model

because of the solution time of the real case input data. Following the introductory

section of this thesis, a comprehensive literature review is carried out in the second

section. Problem definition and solution approach is treated in the third section where

Solution approach, definition of the general structure and formulation of the model and

the results of the pertinent analysis is presented. Here in the last section comprises

conclusions and include proposals from future research of the study and a generalized

summary of the results welded by the proposed model.

3

2. LITERATURE REVIEW

Disaster is a shocking event and caused by nature, technologies and human. It contains

high levels of uncertainty because of difficulty to detect location and time and severely

damages functioning of a community or society by causing human, material and

economic or environmental losses that cannot be accomplished by using local resources

(Caunhye et al., 2012; Galindo and Batta, 2013; Ersoy and Börühan, 2013; Gösling and

Geldermann, 2014; Holguín-Verasa et al., 2013; Özdamar and Ertem, 2014;

Wisetjindawat et al., 2014). Disaster types and disaster inputs/outputs are seen in Figure

1.1.

Figure 1.1. Disasters’ types and inputs/outputs (modified from Can, 2005)

As a result of the definition to decide whether or not a situation is a disaster, the

following three are questions of great importance.

1) Does the event have a badly destructive effect on the functioning of a society?

2) Are the local resources enough in order to deal with effects of the event?

3) Are non-local agencies needed to get involved through a conventional or non-

standard procedure?

An airplane crash is the most common example of this decision. When this case

investigates the idea of this questions, it is not a disaster because it can be conduct by

local agencies with standard procedures. On the other hand, if a terrorist attack is the

DISASTERS

Natural Disasters

Man-Made or Technological

Disasters

Drought

Flood

Landslide

Volcanic Eruption

Earthquake

Tornado

Hurricane Nuclear and Chemical Accidents

Huge fire

War Illegal

Construction

Dam brake

Terror

Lack of Energy or Resources

Limited Resources

Huge Demand

Fatal Emergencies

Human Losses

Material Losses

Economic/Environmental

4

reason of the crash, then it would be called a disaster because this case requires the

support of numerous organizations including organizations in the country (Galindo and

Batta, 2013).

2.1. Disaster Operation Management and Humanitarian Logistics

Disaster operation management is a set of activities that reduce the effect of the disaster

on the economy and human casualties. It is used to return the society to its normal

situation (Galindo and Batta, 2013). Disciplines of logistics management, project

management, risk management, operation research, and resource usage techniques,

information management systems, geographical systems etc. are the most popular ones

to predict and deal with the disaster effects.

As in all phases of life, in case of disaster, resources are limited and demands are huge

and emergencies fatal, so that available resources must be used efficiently and in a

coordinated manner. In this phase integrated disaster management (IDM) system is used

to coordinate all these resources. The basic principles of the IDM are (Can, 2005);

Research all risks,

Apply all phases,

Use all resources efficiently,

Ensure that all the individuals and organizations participate (public institutions

and organizations, non-governmental organizations, population)

Humanitarian logistics deals with activities to reduce health, material and wealth

problems which occur such as destruction of emergency facilities, communicable

diseases, supply of water, food and energy. Examples of these activities are

determination of emergency relief facility location, planning of the distributions of the

relief items, determination of the evacuation centers location, building these evacuation

centers and transportation of the emergency needs and similar products to the required

location and the person, on required time (Börühan et al., 2012; Ersoy and Börühan,

2013; Holguín-Verasa et al., 2013). The most important rule of humanitarian logistics is

the right material is delivered to the right person, in the right amount, the right way, at

the right time and the right place (Tanyaş et al., 2013).

5

Differences of humanitarian logistics from business logistics are summarized as

follows:

Unpredictability of requirement, under the time, location, variety, and dimension

constraints (Kova´cs and Spens, 2009);

Uncertainty of networks (Holguín-Verasa, et al., 2013);

Suddenness of the event of requirement in high quantity however with short lead

times for an extensive variety of supplies (Kova´cs and Spens, 2009);

The efficiency is related with the timeliness of dispatch (Kova´cs and Spens,

2009);

An absence of resources in circumstances of supply, human, technology,

transportation capacity, and money (Kova´cs and Spens, 2009);

Neutral pursued (Holguín-Verasa, et al., 2013);

Lose its significance of cost minimization applications which are especially

done for during disaster (Tanyaş et al., 2013).

A conception model can be used to identify the challenges faced in humanitarian

logistics problem at the beginning of the project and this model is seen in Figure 2.1.1

(Kova´cs and Spens, 2009).

Figure 2.1.1. Challenges of humanitarian logistics (Kova´cs and Spens, 2009)

Humanitarian operations have complex structure because of some factors such as the

following (Wisetjindawat et al., 2014):

Disaster Types

Focus and location of the

humanitarian organization

Stakeholder environment

• Disaster cause (natural/man-made)

• Warning time (cost)

• Probability of disaster in the region

• Mandate related to phase of relief

• Regional presence

• Dependence on declaration of state

• Internal etc., external challenges • Relevant other organizations

Identify challenges

based on

Dimensions

6

1) Numerous different actors like volunteers, victims, governmental and non-

governmental agencies, and logistics companies (see Fig. 2.1.2).

Figure 2.1.2 Stakeholders in Humanitarian Logistics (Wisetjindawat et al., 2014)

2) Distinct phases of relief efforts like the first one is minimum requirements for

survival, second one is some victims are able go back to their homes while some

victims whose houses were seriously damaged still remain at shelters. The third phase is

when most victims are able to go back home but some need to stay longer and are

moved to temporary houses. For the fourth phase, although victims have resumed

normal life, they still need some support in order to restore their quality of life faster;

there are still some donors wishing to provide support (see Fig. 2.1.3).

3) Particular resource requirements (including materials, goods, and people).

Figure 2.1.3. Phases of relief efforts (Wisetjindawat et al., 2014)

All this complexity should be taken into account in the overall planning and operation

of humanitarian logistics, to make them smarter, faster, more reliable and also no more

Donors

Victims

Government Agencies

Non-Government Agencies

Logistics Companies

Phase

1

Phase

2

Phase

3

Phase

4

Need

s o

f vi

ctim

s

Disaster Life at shelter Life at temporary house Normal life

Time

Interactions

7

expensive than necessary. In addition, time constraints in humanitarian logistics during

survival period are more crucial than usual, because faster relief operations mean a

greater likelihood of saving lives (Wisetjindawat et al., 2014).

2.2. Disaster Management / Humanitarian Logistics Categories

Disaster operations and humanitarian logistics are tackled in three categories is pre-

disaster, during disaster, post-disaster activities. They consist of including four phases

mitigation, preparation, response and recovery. Preparation and mitigation are the pre-

disaster phases which importantly decrease the economic, social and physical effects of

a disaster. The examples of these phases are pre-positioning of critical supplies,

improvement of structures, and the development of response plans. Response and

recovery are the post-disaster phases. Determination of the pre-disaster preparation and

planning the coordination of the post-disaster activities have vital importance in easing

uncertainties and reducing huge casualties which occur after the disaster. After disaster,

management of search and rescue operations, health care services, shelter, debris

removal, and logistics of the demands in limited time is very important (Caunhyeet al.,

2012; Galindo and Batta, 2013; Ersoy and Börühan, 2013; Gösling and Geldermann,

2014; Holguín-Verasa et al., 2013; Özdamar and Ertem, 2014; Wisetjindawatet al.,

2014). The disaster relief process can be seen as a cycle linking the recovery and

preparedness phases as seen in figure 2.2.1 (Kova´cs and Spens, 2009).

Figure 2.2.1. Categories and Phases of Disaster / Humanitarian Logistics (Modified

from Kova´cs and Spens, 2009)

Preparation

Mitigation

Response

Recovery

Pre-Disaster

Post-Disaster During Disaster

8

A wide range of humanitarian logistics activities comprises post disaster activities. The

planning of post disaster activities and the need to create a network derive from a

number of requests to eliminate the negativity that may arise after the disaster.

Examples of these activities; comprise allocation of temporary warehouses in bigger

cities, distribution centers and drop-off points, determination of network which include

such activities like plans of decreasing the errors of logistics network where transport

corridors may be broken, unsafe or insecure, planning and implementation the

communication network between operational units (private sector, specialized

military/non-military institutions, government agencies, The Disaster and Emergency

Management Authority (AFAD)). Pre-disaster and post-disaster activities are seen in

table 2.2.1 (Gösling and Geldermann, 2014).

Table 2.2.1. Pre-disaster and Post-disaster Activities of Humanitarian Logistics

(Gösling and Geldermann, 2014)

Pre-disaster activities Post-disaster activities

Determining the location of permanent

warehouses, suppliers and the material

to be stored.

Determining the type, quantity,

capacities, locations, and suppliers of

the transportation vehicles.

Determining the location and the

number of professional man power.

Determining the routes and the

schedules for evaluation teams

Determining the location, capacities

of permanent warehouses and

distribution centers and material to

be stored in there.

Determining the locations of

delivery points in the affected

settlements

Determining the locations of non-

priority donation’s separation

centers.

Determining the quantities and the

location of the professional and

volunteer workers.

9

Table 2.2.1-continue Pre-disaster and Post-disaster Activities of Humanitarian

Logistics (Gösling and Geldermann, 2014)

Pre-disaster activities Post-disaster activities

Identification of suppliers and

supply orders for relief materials.

Determining the types, loads, routes

and schedules of delivery vehicles

2.2.1. Pre-disaster categories

Pre-disaster activities are long or short term activities that exercise instrumental role in

strategic planning or disaster mitigation. These activities aim at reducing the devastating

effects of disasters and protecting the health and wealth of the communities (Can,

2005). Examples of this category are facility location, stock pre-positioning, evacuation,

training, determining unsuitable constructions, etc. (Caunhye et al., 2012). The main

purpose of preparedness plans and risk-prevention actions is to reduce the destructive

effects of the disaster. Stochastic models are used due to the high level of uncertainties

such as the impact level, time and location of disaster (Wisetjindawat et al., 2014).

2.2.1.1. The preparation phase

The aim of the preparation phase is to take corrective action in order to alleviate or

eradicate the impact of the disaster on community in the most proper way and the most

efficient organizations and procedures on time (Can, 2005). Building codes, education

and practice and insurance regulations related to land usage are the examples for this

phase. Preparation of disaster starts with risk analysis and damage visibility which are

used to plan the disaster relief operations efficiently. This is followed by planning

studies, evaluating information systems, planning the efficient use of resources and

facilities and determining the location of new facilities. If a problem occurs, warning

system generates the disaster response mechanism and planning the public awareness

education in order to prepare the people for disaster and help them understand the

mechanism. Last but not least, “practice” is prerequisite to better coordination and

10

reducing mistakes during action (Wisetjindawat et al., 2014). This system is illustrated

in Table 2.2.1.1.1.

The efficiency of disaster relief operations is very dependent on the quality of the

activities which are carried out the preparation phase. This is taken to mean that the rate

of saving lives increases with efficient use of resources. Success of disaster

preparedness is scaled with estimating the requirements of survivors, and the estimated

capacities of the planned supply chains. This preparation helps to have information

about such as the capacities of facilities, and the coordination plan availability about

vehicles and personnel. So that, these activities can be quickly organized with these

plans on recovery phase (Wisetjindawat et al., 2014).

Table 2.2.1.1.1. Topics of Pre-Disaster (Güler, 2005)

Preparation of

disaster

Risk Analysis and

Damage Visibility Planning Studies Institutional Studies

Information Systems Resources and

Facilities Warning Systems

Disaster Response

Mechanisms

Disaster Awareness and

Public Education Practice

2.2.1.2. The mitigation phase

The Mitigation phase starts after the activities of recovery and rebuilding phases, and

continues until a new event occurs. All precaution any activities should be carried out

during this phase in order to prevent the effect of disaster and huge causalities. The

activities of this phase are training and practice, reviewing the procedures of the during

disaster category, reviewing building and the earthquake regulations, insurance

activities, etc. (Can, 2005)

2.2.2. Post-disaster categories

Post disaster category of activities commences immediately after a disaster has

occurred. This category is subdivided into long-term and short-term activities. This

category starts with the response phase activities which starts immediately after a

11

disaster and continues until the end of the recovery phase (Özdamar and Ertem, 2014;

and Can, 2005).

2.2.2.1. The response phase

The aim of this phase is to enhance survival of the injured people, ensuring the

treatment of the injured and the provision of the minimum requirements of basic

necessities such as water, food, clothes, sheltering, and security. Search and rescue

operations, allocation of vehicles and emergency needs, relocation of disaster victims,

impact assessment activities, and investigation of the required repairs are the activities

of this phase (Özdamar and Ertem, 2014; and Can, 2005).

Depending on the size of the disaster, these activities are carried out in one or two

months.

Logistics models for the response phase are reviewed in two major categories:

a) Relief delivery/casualty transport models,

b) Mass evacuation models. (Özdamar and Ertem, 2014; and Can, 2005)

2.2.2.2. The recovery phase

The aim of the recovery phase is to make all the efforts required for communities

affected by the disaster to attain and sustain a minimum level of vital activity such as

communication, transportation, water services, energy, sewerage services, education,

long-term temporary housing, economic and social activities, etc. (Can, 2005).

This phase consists of short term and long term activities. During short term activities,

chaotic and challenging situations exist because needs are extreme and resources are

limited. The volunteer management, debris removal, the restoration of critical

infrastructure, procurement of materials and their distribution take place during this

term. On the other hand, the goal of the long term recovery activities is to return to

normal way of life. The delivery of food and medicines to affected areas is the activity

of this phase (Holguín-Verasa, et al., 2013).

12

2.3. Applications in Turkey: Previous Studies

Disaster management and disaster / humanitarian logistics issues have found huge space

in the pertinent literature which focuses on studies relevant to Turkey. Literature on the

subject is researched and after the classification study (see in the Appendix 1 and

Appendix 2), gaps in the literature covering issues for Turkey were investigated.

Disaster management and disaster / humanitarian logistics issues of implementation is

observed mostly the studies concerning İstanbul or Ankara. Comprehensive studies

need to be done for other regions associated with risks. In terms of the type of disaster,

only the earthquake issue was studied. Clearly other topics associated with very high

risks for Turkey such as floods, terrorism, possible problems related to energy sources,

etc. cannot be ignored. Studies on certain issues such as volunteer management,

allocation and coordination of search and rescue teams, efficient usage of resources

such as assigning vehicles for debris removal case have been found to be lacking.

2.4. Debris Removal Operation

Debris removal operations start after search and rescue operations are completed. They

are post-disaster, recovery phase activities. They are complex problems because of huge

demand and high level uncertainty which is due to the difficulties associated with

making reliable estimation of the size of destruction is real terms. Considering the large

amount of debris waste produced after disasters debris removal management assumes

vital importance due to its impaction on the environment and the health of residents

exposed to it. If the debris is not collected with appropriate and accurate methods, the

land, underground water and the surrounding environment may become contaminated.

Improperly stacked debris can cause various hazards including environmental pollutions

such as land, air quality, and water system. Additionally, pose serious danger citizens

who attempt to recover recyclable scrap without any permission (Hu and Sheu, 2013;

Palabıyık, 2000; T.C. Tekirdağ Valiliği İl Çevre ve Orman Müdürlüğü).

Debris has different characteristics depend on the nature of event such as renovation,

construction, demolition activities and disaster. Although components of construction

and demolition waste vary depending on the content of the materials used in

construction, debris is produced from reinforced concrete, concrete, brick, cinder block,

13

briquette, wood, glass, metal (steel, aluminum, copper, brass, iron), plaster, drywall,

tile, plastic, electrical components, pipes. Reinforced concrete, concrete, cinder block,

briquette are used as aggregate for some type of applications after breaking and

crushing operations which are time consuming and expensive processes. However, this

recycling process preserves primary aggregate resources and considerably diminishes

pollution effects on the environment of affected sites. If debris is stacked without any

systematic procedure at landfill, soil may be contaminated resulting in prevention of

plant growth in that area. Similarly, debris deposited on river, batches can cause the

narrowing and changing of the riverbed. When discharged near roads debris can cause

bad view, pollution of soil and blockage of channels (Hu and Sheu, 2013; Palabıyık,

2000; T.C. Tekirdağ Valiliği İl Çevre ve Orman Müdürlüğü). Figure 2.4.1 illustrates

such a bad practice.

Many countries have banned the uncontrolled disposal of such waste. Since the

aggregate resources are limited, especially the use of construction and demolition waste

as secondary raw materials should be encouraged in Turkey. Also the cleaning process

of uncontrolled pouring of debris means extra cost and manpower (Hu and Sheu, 2013;

Palabıyık, 2000).

Figure 2.4.1 An image of an uncontrolled disposal of debris (T.C. Tekirdağ Valiliği İl

Çevre ve Orman Müdürlüğü)

Another important aspect of the disaster management is debris operations. Pre-disaster

activities are of great significance since they may be designed to reduce the cost of

debris removal operations. Pre-disaster debris removal operations start with estimation

14

of debris size by using special techniques. Removal operations are followed by

preparation of debris collection, reduction /recycling and final disposal strategies and

procedures: Finally, debris management site planning is accomplished. Training of

debris management teams are important for the proper implementation of the processes

identified in the planning during pre-disaster, mitigation phase. Building inspections are

vital importance, since through too in order to this practice leading to due measures the

size of destruction can be considerably reduced. Immediately after disaster, Debris

management sites operations and debris clearance start concurrently, search and rescue

operations must begin rapidly and effectively. Pre-disaster activities have important

roles to achieve effective debris management sites operations which depend on the

appropriate, timely and quick coordination of teams. Clearing debris from emergency

routes is an operation which need to be done as fast as possible since human life is

concerned. While debris clearance is a response phase activity, debris management site

operations start at the response phase and continue during the recovery phase too.

Debris collection, recycling, and final disposal operations are also a part of debris

management site operations. Categories and Phases of debris operations are depicted in

Figure 2.4.2 (Hu and Sheu, 2013; Şahin, 2013).

When examining international practices highly successful operations the case of the Los

Angeles earthquake which took place on January 17, 1995. Stands out Los Angeles city

debris management strategies helped reduce the cost of debris removal operations.

About 76 percent of the debris was recycled so that 4445000 cubic meters of landfill

were saved. The debris management strategies have three important rules (Palabıyık,

2000);

1. Dissemination of information: man power for debris removal process was

educated regarding what should be done after disaster. Debris collection process

was conducted with the orientation of the phone calls from debris removal and

transfer companies and citizen.

2. Recycling: Every citizen is obligated to recycle his/her own debris. Failing to do

that the recycling companies are allowed to do the job. The effort of the local

people provides a reduction of the debris removal cost and increase

effectiveness.

15

3. Exchange of materials: Some community groups have been employed by the

city administration of Los Angeles for the separation process of bricks, iron and

wood from debris. This is especially attractive for lots income people who can

use these materials for their own business.

Figure 2.4.2 Categories and Phases of Debris Management (Modified from Şahin,

2013)

While international operations subject to the best practices, it has attracted the attention

of a certain section of society in our country. When previous national operations are

examined, it is understanding that new more coordinated and prepared debris

management site plan is needed. After Erzincan earthquake on March 13, 1992, debris

was poured uncontrolled near roads, around town that may be detrimental to the

environment and blocked traffic. Debris operations were acted in that vein after

Marmara earthquake on August 17, 1999. Construction and rubble wastes were poured

into the sea, the river reaching the sea and regularly on different areas at Değirmendere

landfill, Izmit. Debris removal operations were tendered to 10 subcontractors in

Pre-Disaster Preparation

Post-Disaster Response

Post-Disaster Recovery

Estimation of

debris and scenario analysis

Debris Clearance Debris Collection

Procedures and strategies of

collecting debris operations

Debris Management Sites Operations

Debris Management Sites Planning

Debris Reduction / Recycling

Debris Final Disposal

Pre-Disaster Mitigation

Team Training

Building inspections

Team Drill

16

Sakarya. In the first days following the disaster, they were poured uncontrolled way

near Sakarya River and part of swamp areas which are determined by province. Most of

citizen who attempted to separate iron from stack on debris final disposal area were in

danger because of uncontrolled access to the area (Palabıyık, 2000).

2.4.1. Debris recycling operation

Debris recycling operation may become necessary in order to conserve primary

aggregate resources and reduce the harmful effect on environment associated with

debris. In fact cost of debris removal operations can be relatively decreased by well-

planned recycling operations as in the Los Angeles example. The techniques for

collecting, recycling and final disposal of construction and demolition waste are shown

in Figure 2.4.1.1, 2.4.1.2 and 2.4.1.3 (Öztürk, 2005).

Figure 2.4.1.1 Basic approach of Debris recycling operation (Öztürk, 2005)

Figure 2.4.1.2 Combined approach of Debris recycling operation (Öztürk, 2005)

Figure 2.4.1.3 Approach of Debris recycling operation with using recycling machines

(Öztürk, 2005)

Recycling operation starts classification of the materials process. Before planning and

classification of materials, economical evaluation should be done considering labor and

Debris Manual Separation Recycled materials

Final Disposal Areas

Debris Screening Recycled materials

Final Disposal Areas

Separation at

the band

Debris Crushing Recycled materials

Final Disposal Areas

Mechanical

Separation

17

transportation costs (Öztürk, 2005). Recycling machines which are used for urban

renewal projects in İstanbul are seen in Figure 2.4.1.4. Recycling process starts with

screening process, separation of soil and aggregate, that the final product should be

smaller than 38 mm. If the final product’s specifications are not between limits, it is sent

to the final disposal area. Big size rubbles are crushed mechanically so that iron scrap is

separated by using a magnetic band and also different size of aggregates are separated

by screening process. At the recycling facility in Istanbul (İSTAÇ) the aggregate size

fall within the range 0 mm – 38 mm.

Figure 2.4.1.4 Recycling machines are used at facility in Istanbul (İSTAÇ)

Scientific studies have revealed that recycled aggregate is suitable to use at places

where seismic load and physical effects are less. 0-12 mm aggregate is suitable to be

used as backfill concrete of pipelines. 12-22 mm aggregate is recommended to be used

as light path and pavement filler, while 13-22 and 23-38 mm aggregates are suitable to

use in backfilling of building and retaining wall and as drainage channel and backfill

material (İSTAÇ).

3. PROBLEM DEFINITION AND SOLUTION APPROACH

Due to the scarcity of relevant data, and considering the enormous number of the houses

in the disaster zone it is needed to be prepared for the impact of disaster since it is not

possible estimating time and size of disaster. In the view of the fact that it is not

possible to forecast the time and magnitude of the impending disaster, it is imperative to

take due precautions to counter its adverse effects.

screening machine Crushing and

screening machine

18

Considering the literature review part, there is a lack of scientific studies for debris

removal process in Turkey. In this thesis, the vehicle assignment problem was studied

for debris removal operation.

3.1. Solution Approach

Multi-objective mathematical programming is considered as the simultaneous

realization of multiple objectives in a mathematical programming structure. There are

many reasons of increasing interest in multi-purpose mathematical programming. The

first and most important one is that nature of many decision problems has multi

objective functions such as a debris removal operation aims to minimize costs of

operations/decisions, on the contrary aims to maximize amount of recycled material.

The other reason why the multi objective models have importance is that most of

production planning problems must meet standards like ISO 9001, TS16949, etc. The

last reason is ease and speed of computing solutions is ensured to solve the

multipurpose problem. In particular, it has reflected the developments in computer

problem solving (Atlas, 2008).

When existing literature is investigated, numerous methods are available to combine

objective functions of multi objective models such as multi objective linear

programming, goal programming, fuzzy theory, weighting technique, hierarchical

separation technique, normalization etc. In this study, Linear Physical Programming

(LPP) is used to solve the proposed model. LPP method uses crisp numbers, on the

other hand objective functions are piecewise linear which makes allowance to obtain

desirability levels of decision makers. In LPP, the designer does not need to decide the

weights of the objective functions in the problem formulation phase. On the contrary,

the designer need to specify ranges of different levels of desirability for each objective

functions. LPP provides a flexible and more deterministic approach to obtain a solution.

LPP explain criteria with using four different class types by declaring that each belong

to one of the classes. A criterion of a class is desired to be determined at two situations

as hard and soft. Figure 3.1.1 depicts the qualitative and quantitative meanings of each

soft class. The value of criterion under consideration, OFg, is shown on the horizontal

axis, and the function that will ve minimized for that criterion, zi, is shown on the

vertical axis. The preference ranges are (Maria, et al., 2003; Gulsun et al., 2009):

19

Ideal range OFg ≤ tg1

Desirable range tg1 ≤ OFg ≤ tg2

Tolerable range tg2 ≤ OFg ≤ tg3

Undesirable range tg3 ≤ OFg ≤ tg4

Highly Undesirable range tg4 ≤ OFg ≤ tg5

Unacceptable range OFg ≤ tg5

The parameters tg1 through tg5 are physically meaningful constant that generate from

designer (Maria, et al., 2003; Gulsun et al., 2009).

For Class 1S, Maria et al. (2003) additional comments: “Consider the first curve of the

Figure 3.1.1, when the value of OFg is less than t+

g1, the value of the class function is

small and requires little further minimization. On the other hand, when the value of OFg

is between t+

g4 and t+

g5, the value of the class function is large and requires significant

minimization.”.

Most important properties of the class functions are:

i. Nonnegative, continuous, piecewise linear and convex

ii. zg at a given target level is the same for all class types.

LPP determines the weights (ŵ+

gs and ŵ-gs see Eq. (1)) which represent the incremental

slope of the class functions, zg. Negative and positive deviations of the objective value

OFg are represented as d-gs and d

+gs (Maria, et al., 2003).

𝑀𝑖𝑛 𝐽 = ∑ ∑(�̌�𝑔𝑠− × 𝑑𝑔𝑠

− + �̌�𝑔𝑠+ × 𝑑𝑔𝑠

+ )

5

𝑠=1

𝐺

𝑔=1

(1)

Subject to

𝑂𝐹𝑔 − 𝑑𝑔𝑠+ ≤ 𝑡𝑔,𝑠−1

+ ; 𝑑𝑔𝑠+ ≥ 0 ; 𝑂𝐹𝑔 ≤ 𝑡𝑔5

+

(for classes 1S, 3S, 4S; g = 1,…,G; s = 2,…,5)

(2)

𝑂𝐹𝑔 − 𝑑𝑔𝑠− ≥ 𝑡𝑔,𝑠−1

− ; 𝑑𝑔𝑠− ≥ 0 ; 𝑂𝐹𝑔 ≥ 𝑡𝑔5

−

(for classes 2S, 3S, 4S; g = 1,…,G; s = 2,…,5)

(3)

Eq. (1) shows the minimization of weighted deviations of the objective functions. Eq.

(2) applies the criteria belonging to all classes except Class 2S, on the other hand Eq.

(3) applies to criteria belonging to all classes except Class 1S (Maria, et al., 2003).

20

Figure 3.1.1. Class functions for linear physical programming (Maria et al., 2003)

Ž 3

Ž 4

Ž 5

Ž 2 Ž

3

Ž 4

Ž 5

DE

SI

RA

BL

E

TO

LE

RA

BL

E

UN

DE

SI

RA

BL

E

HI

GH

LY

UN

DE

S

UN

AC

CE

PT

AB

LE

UN

AC

CE

PT

AB

LE

HI

GH

LY

UN

DE

S

UN

DE

SI

RA

BL

E

TO

LE

RA

BL

E

DE

SI

RA

BL

E

Ž 2

Ž 3

Ž

4

Ž 5

UN

AC

CE

PT

AB

LE

HI

GH

LY

UN

DE

S

UN

DE

SI

RA

BL

E

TO

LE

RA

BL

E

DE

SI

RA

BL

E

ID

EA

L

Ž 3

Ž

4

Ž 5

Ž 2

DE

SI

RA

BL

E

UN

DE

SI

RA

BL

E

HI

GH

LY

UN

DE

S

UN

AC

CE

PT

AB

LE

TO

LE

RA

BL

E

ID

EA

L

Ž 3

Ž

4

Ž 5

Ž 2

Ž 3

Ž

4

Ž 5

DE

SI

RA

BL

E

TO

LE

RA

BL

E

UN

DE

SI

RA

BL

E

HI

GH

LY

UN

DE

S

UN

AC

CE

PT

AB

LE

UN

AC

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PT

AB

LE

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UN

DE

SI

RA

BL

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TO

LE

RA

BL

E

DE

SI

RA

BL

E

ID

EA

L

t+

g1 t+

g2 t+

g3 t+

g4 t+

g5

t+

g1 t

+

g2 t+

g3 t+

g4 t+

g5 t

-

g5 t-

g4 t-

g3 t-

g2

t-

g5 t

-

g4 t-

g3 t-

g2 t-

g1

t+

g1 t

+

g2 t+

g3 t+

g4 t+

g5 t

-

g5 t

-

g4 t-

g3 t-

g2 t-

g1

OFg

OFg

OFg

OFg

zg

zg

zg

zg

CLASS 1S

CLASS 2S

CLASS 3S

CLASS 4S

1

3.2. General Structure and Formulation of Model

Debris removal operations have vital importance to reduce the effects on nature, human

and economy. Because of this situation the proposed model is designed considering

recycling amount of debris, cost of debris removal process, negative impact of it on

victims and emission index to calculate the effect of nature. One objective function

model is not enough to have realistic case and to get desired results.

Three objective functions model was considered for this case. First objective function

(OF1) minimizes the total cost of the vehicle assignment, collection, transportation,

recycling, demolishing processes etc. (see Eqs. (4)-(11)). It was emphasized in the

literature that the cost is negligible. For this case, time was considered as a cost.

The second objective function (OF2) minimizes the negative impact of amount of

disaster on the affected people (see Eq. (12)). It was aimed where population rate is

higher, debris should be removed there quickly.

The last objective function (OF3) minimizes the emission index (see Eqs. (13)-(17)).

During disaster time is most important one, on the other hand, it has importance while

the rate of climate changes increases.

In the proposed model, the main goal is assigning vehicles from vehicle location area to

the Disaster Affected Sites (DAS) and Temporary Storage Area (TSA) considering the

debris amount. Vehicle assignments are taken based on the amount of debris in the area

at the beginning of each semester. While heavily damaged buildings are destroyed

debris is transported from affected area i to temporary storage areas. These vehicles can

also be assigned to the temporary storage areas considering amount of the debris on the

places. After first period completed second period is started and debris is sent from

affected areas to temporary storage areas. After recycling operation, useless materials

are sent to disposal areas. The proposed model is shown on Figure 3.2.1.

The most imported point is that excavator is used for demolishing activity so is sent

based on heavily damaged buildings amount. On the other hand, loader and truck are

sent based on debris amount at disaster affected area. Loader is used to load debris on

truck so total loader’s capacities should be equal to total truck capacities. The other

point is recycling operation is done at only temporary storage areas.

2

Figure 3.2.1. Problem definition

Source of Vehicle

( j )

Private Public

X2jiet

X3jiet

X1jiet

Disaster affected area ( i )

Amount of

debris

(ait)

aa

i

The number of

heavily damaged

buildins

(a1it)

aa1

i

ymit

Temporary storage area ( k )

Amount of

debris (qkt

)

gdmkt

tmikikt

Z

iket

Disposal

area ( d )

tmkdkdt

Z1

kdet

tmikikt

tmididt

3

Definitions of index, variables and parameters of the proposed model are seen in

Appendix 8.

The objective functions of the model

Minimization of net total cost (OF1):

OF1 = OF11 + OF12 + OF13 + OF14 + OF15 + OF16 - OF17 (4)

𝑂𝐹11 = ∑(𝜑𝑘 × 𝜇𝑘)

𝐾

𝑘=1

(5)

𝑂𝐹12 = ∑ ∑ ∑(𝜑1𝑘 × 𝑡𝑚𝑖𝑘𝑖𝑘𝑡)

𝑇

𝑡=1

𝐾

𝑘=1

𝐼

𝑖=1

(6)

𝑂𝐹13 = ∑ ∑ ∑ ∑[(𝑋1𝑗𝑖𝑒𝑡 + 𝑋2𝑗𝑖𝑒𝑡 + 𝑋3𝑗𝑖𝑒𝑡) × (𝑅𝑗𝑒 + 𝑀𝑗𝑖 + 𝑑3𝑗𝑖 × 𝐵)]

𝑇

𝑡=1

𝐸

𝑒=1

𝐼

𝑖=1

𝐽

𝑗=1

+

∑ ∑ ∑ ∑[(𝑋4𝑗𝑘𝑒𝑡 + 𝑋5𝑗𝑘𝑒𝑡) × (𝑅𝑗𝑒 + 𝑀1𝑗𝑘 + 𝑑4𝑗𝑘 × 𝐵)]

𝑇

𝑡=1

𝐸

𝑒=1

𝐾

𝑘=1

𝐽

𝑗=1

(7)

𝑂𝐹14 = ∑ ∑ ∑ ∑ 𝑛1 × 𝑋1𝑗𝑖𝑒𝑡 × 𝑐1

𝑇

𝑡=1

𝐸

𝑒=1

𝐼

𝑖=1

𝐽

𝑗=1

(8)

𝑂𝐹15 = ∑ ∑ ∑ ∑(𝑛2 × 𝑐2 × 𝑋2𝑗𝑖𝑒𝑡)

𝑇

𝑡=1

𝐸

𝑒=1

𝐼

𝑖=1

𝐽

𝑗=1

+ ∑ ∑ ∑ ∑(𝑛2 × 𝑐2 × 𝑋4𝑗𝑘𝑒𝑡)

𝑇

𝑡=1

𝐸

𝑒=1

𝐾

𝑘=1

𝐽

𝑗=1

(9)

𝑂𝐹16 = ∑ ∑ ∑ ∑(𝑛3 × 𝑐3 × 𝑑2𝑖𝑘 × 𝑍𝑖𝑘𝑒𝑡)

𝑇

𝑡=1

𝐸

𝑒=1

𝐾

𝑘=1

𝐼

𝑖=1

+

∑ ∑ ∑ ∑(𝑛3 × 𝑐3 × 𝑑1𝑘𝑑 × 𝑍1𝑘𝑑𝑒𝑡)

𝑇

𝑡=1

𝐸

𝑒=1

𝐷

𝑑=1

𝐾

𝑘=1

(10)

𝑂𝐹17 = ∑ ∑ ∑ 𝐺𝑝 × 𝑔𝑑𝑝𝑚𝑘𝑝𝑡

𝑇

𝑡=1

𝑃

𝑝=1

𝐾

𝑘=1

(11)

The first objective function is the minimization of net total cost which is obtained by

subtracting income from total costs. Total costs are obtained as the summation of cost of

opening temporary storage area (OF11) which is calculated by multiplying the cost of

4

opening TSA and decision of opening TSA (5), cost of recycling debris at TSA (OF12)

which is calculated by multiplying the cost of recycling operations and amount of debris

transported from DAS to TSA (6), cost of allocation of vehicles from source of vehicles

o DAS (OF13) which is sum of multiplication of assigned vehicles amount and cost of

emission prefer, service and fuel (7), demolition cost (OF14) which is calculated by

multiplying the demolition cost, capacity of vehicles (is assumed equal for all vehicles

among each other) and amount of assigned excavator (8), loading cost (OF15) which is

calculated by multiplying loading cost, capacity of vehicles and amount of assigned

loader (9), cost of transporting vehicles from DAS to TSA and TSA to disposal area

(OF16) which is calculated by multiplying transportation cost, capacity of truck,

distances between locations and number of transported vehicles (10). Revenue of

recycled materials which is obtained from (OF17) multiplying sales cost and amount of

recycled materials (11), is assumed that purchaser is responsible for recycled materials

transportation.

Minimization of negative impacts (OF2):

𝑀𝑖𝑛 ∑ ∑((𝐴𝑖𝑡 + 𝐴1𝑖𝑡) × 𝑛𝑒𝑖 × 𝑛𝑖𝑟𝑎𝑡𝑒𝑡)

𝑇

𝑡=1

𝐼

𝑖=1

(12)

OF2 obtain multiplication of sum of debris (estimated amount of debris and number of

destroyed buildings), population ratio of DAS i and negative impact rate on i at period t

(12). The negative impact at TSA is ignored for the proposed model.

Minimization of Emission Rate (OF3):

Min OF31 + OF32 + OF33 (13)

𝑂𝐹31 = ∑ ∑ ∑ ∑[(𝑋1𝑗𝑖𝑒𝑡 + 𝑋2𝑗𝑖𝑒𝑡𝑋3𝑗𝑖𝑒𝑡) × 𝑑3𝑗𝑖 × 𝑒𝑑𝑒]

𝑇

𝑡=1

𝐸

𝑒=1

𝐼

𝑖=1

𝐽

𝑗=1

(14)

𝑂𝐹32 = ∑ ∑ ∑ ∑[(𝑋4𝑗𝑘𝑒𝑡 + 𝑋5𝑗𝑘𝑒𝑡) × 𝑑4𝑗𝑘 × 𝑒𝑑𝑒]

𝑇

𝑡=1

𝐸

𝑒=1

𝐾

𝑘=1

𝐽

𝑗=1

(15)

𝑂𝐹33 = ∑ ∑ ∑ ∑(𝑍𝑖𝑘𝑒𝑡 × 𝑑2𝑖𝑘 × 𝑒𝑑𝑒)

𝑇

𝑡=1

𝐸

𝑒=1

𝐾

𝑘=1

𝐼

𝑖=1

(16)

5

𝑂𝐹34 = ∑ ∑ ∑ ∑(𝑍1𝑘𝑑𝑒𝑡 × 𝑑1𝑘𝑑 × 𝑒𝑑𝑒)

𝑇

𝑡=1

𝐸

𝑒=1

𝐷

𝑑=1

𝐾

𝑘=1

(17)

OF3 is obtained by multiplying emission penalty of e emission indexes, number of

assigned vehicles and distances (14)-(17).

These three objective functions affect each other as follows:

i. If collection time of debris reduces, net total cost and emission penalty cost

would increases because of larger number of assigned vehicles at first period

instead of assigned through further time periods. As a result of this situation,

emission rate of vehicles might increase.

ii. If low emission index is preferred net total cost would increase because of

hiring cost of new model vehicles. For this model, fuel cost differences of

vehicles (new model consume less fuel) is ignored.

These three objective functions are combined by using LPP technique which is

explained at section 3.1 and equations (1)-(3).

Constraints:

∑ ∑ ∑ 𝑐2 × 𝑋2𝑗𝑖𝑒𝑡

𝑇

𝑡=1

𝐸

𝑒=1

𝐽

𝑗=1

≥ ∑ ∑ ∑ 𝑐3 × 𝑋3𝑗𝑖𝑒𝑡

𝑇

𝑡=1

𝐸

𝑒=1

𝐽

𝑗=1

(∀ 𝑖 = 1,2,3, … , 𝑛) (18)

∑ ∑ ∑ 𝑐2 × 𝑋4𝑗𝑘𝑒𝑡

𝑇

𝑡=1

𝐸

𝑒=1

𝐽

𝑗=1

≥ ∑ ∑ ∑ 𝑐3 × 𝑋5𝑗𝑘𝑒𝑡

𝑇

𝑡=1

𝐸

𝑒=1

𝐽

𝑗=1

(∀ 𝑘 = 1,2,3, … , 𝑛) (19)

∑ 𝑦𝑚𝑖𝑡

𝑇

𝑡=1

= 𝑎𝑎1𝑖 (∀ 𝑖 = 1,2,3, … , 𝑛) (20)

𝑦𝑚𝑖(𝑡+1) ≤ ∑ ∑ 𝐶1 × 𝑋1𝑗𝑖𝑒𝑡

𝐸

𝑒=1

𝐽

𝑗=1

(

∀ 𝑖 = 1,2,3, … , 𝑛 𝑎𝑛𝑑 ∀ 𝑡 = 1, … , 𝑛 − 1

) (21)

𝑦𝑚𝑖1 = 0 (∀ 𝑖 = 1,2,3, … , 𝑛 ) (22)

𝑡𝑚𝑖𝑘𝑖𝑘1 = 0 (∀𝑖 = 1, . , 𝑛; ∀𝑘 = 1, . , 𝑛) (23)

𝑡𝑚𝑘𝑑𝑘𝑑1 = 0 (∀ 𝑘 = 1, … , 𝑛; ∀ 𝑑 = 1, … , 𝑛) (24)

𝑔𝑑𝑚𝑘1 = 0 (∀ 𝑘 = 1,2,3, … , 𝑛) (25)

6

𝑄𝑘1 = 0 (∀ 𝑘 = 1,2,3, … , 𝑛) (26)

𝑎1𝑖(𝑡+1) = 𝑎1𝑖𝑡 − 𝑦𝑚𝑖(𝑡+1) (∀ 𝑖 = 1,2,3, … , 𝑛 𝑣𝑒 ∀ 𝑡 = 1, . . , 𝑛 − 1 ) (27)

𝑎𝑖1 = 𝑎𝑎𝑖 (∀ 𝑖 = 1,2,3, … , 𝑛) (28)

∑ 𝐶3 × 𝑍𝑖𝑘𝑒𝑡

𝐸

𝑒=1

≥ 𝑡𝑚𝑖𝑘𝑖𝑘(𝑡+1) (∀𝑖 = 1, . , 𝑛; ∀𝑘 = 1, . , 𝑛; ∀𝑡 = 1, . , 𝑛 − 1 ) (29)

∑ ∑ 𝑡𝑚𝑖𝑘𝑖𝑘𝑡

𝑇

𝑡=1

𝐾

𝑘=1

= 𝑎𝑎𝑖 + 𝑎𝑎1𝑖

(∀ 𝑖 = 1,2,3, … , 𝑛) (30)

𝑎𝑖(𝑡+1) = 𝑎𝑖𝑡 + 𝑦𝑚𝑖(𝑡+1) − ∑ 𝑡𝑚𝑖𝑘𝑖𝑘(𝑡+1)

𝐾

𝑘=1

(

∀𝑖 = 1, . , 𝑛; ∀𝑘 = 1, . , 𝑛;∀𝑡 = 1, . , 𝑛 − 1

) (31)

∑ 𝐶3 × 𝑍1𝑘𝑑𝑒𝑡

𝐸

𝑒=1

≥ 𝑡𝑚𝑘𝑑𝑘𝑑(𝑡+1) (

∀𝑘 = 1, . , 𝑛; ∀𝑑 = 1, . , 𝑛 𝑎𝑛𝑑 ∀𝑡 = 1, . , 𝑛 − 1

) (32)

𝑔𝑑𝑝𝑚𝑘𝑝𝑡 = ∑ ∑ 𝑡𝑚𝑖𝑘𝑖𝑘𝑡 × 𝛿𝑘𝑝

𝑃

𝑝=1

𝐼

𝑖=1

(

∀ 𝑘 = 1, … , 𝑛; ∀ 𝑝 = 1, … , 𝑛 𝑎𝑛𝑑 ∀𝑡 = 1, … , 𝑛

) (33)

∑ ∑ 𝑡𝑚𝑘𝑑𝑘𝑑𝑡

𝑇

𝑡=1

𝐷

𝑑=1

= ∑ ∑ 𝑡𝑚𝑖𝑘𝑖𝑘𝑡

𝑇

𝑡=1

𝐼

𝑖=1

− ∑ ∑ 𝑔𝑑𝑝𝑚𝑘𝑝𝑡

𝑇

𝑡=1

𝑃

𝑝=1

(∀ 𝑘 = 1, … , 𝑛) (34)

𝑄𝑘(𝑡+1) = 𝑄𝑘𝑡 + ∑ 𝑡𝑚𝑖𝑘𝑖𝑘𝑡 − ∑ 𝑔𝑑𝑝𝑚𝑘𝑝𝑡

𝑃

𝑝=1

𝐼

𝑖=1

− ∑ 𝑡𝑚𝑘𝑑𝑘𝑑𝑡

𝐷

𝑑=1

(

∀𝑘 = 1, . . , 𝑛; ∀𝑡 = 1, . , 𝑛 − 1

) (35)

𝑄𝑘𝑡 ≤ 𝑐𝑘𝑘 × 𝜇𝑘 (∀ 𝑘 = 1, … , 𝑛 𝑎𝑛𝑑 ∀𝑡 = 1, … , 𝑛) (36)

∑ 𝑡𝑚𝑖𝑘𝑖𝑘𝑡

𝐼

𝑖=1

≤ 𝑐𝑘𝑘 × 𝜇𝑘 (∀ 𝑘 = 1, … , 𝑛 𝑎𝑛𝑑 ∀𝑡 = 1, … , 𝑛) (37)

∑ 𝑍𝑖𝑘𝑒𝑡

𝐾

𝑘=1

+ ∑ 𝑍2𝑖𝑑𝑒𝑡

𝐷

𝑑=1

= ∑ 𝑋3𝑗𝑖𝑒𝑡

𝐽

𝑗=1

(

∀𝑖 = 1, … , 𝑛; ∀𝑒 = 1, … , 𝑛 𝑎𝑛𝑑 ∀𝑡 = 1, … , 𝑛

) (38)

∑ 𝑍1𝑘𝑑𝑒𝑡

𝐷

𝑑=1

= ∑ 𝑋5𝑗𝑘𝑒𝑡

𝐽

𝑗=1

(

∀𝑘 = 1, … , 𝑛; ∀𝑒 = 1, … , 𝑛 𝑎𝑛𝑑 ∀𝑡 = 1, … , 𝑛

) (39)

7

∑ 𝑋1𝑗𝑖𝑒𝑡

𝐼

𝑖=1

≤ 𝑓1𝑗𝑒

(∀𝑗 = 1,2, … , 𝑛; ∀𝑒 = 1,2, … , 𝑛 𝑣𝑒 ∀𝑡 = 1,2, . . , 𝑛) (40)

∑ 𝑋2𝑗𝑖𝑒𝑡

𝐼

𝑖=1

+ ∑ 𝑋4𝑗𝑘𝑒𝑡

𝐾

𝑘=1

≤ 𝑓2𝑗𝑒 (

∀𝑗 = 1,2, … , 𝑛; ∀𝑒 = 1,2, … , 𝑛 𝑎𝑛𝑑 ∀𝑡 = 1,2, . . , 𝑛

) (41)

∑ 𝑋3𝑗𝑖𝑒𝑡

𝐼

𝑖=1

+ ∑ 𝑋5𝑗𝑘𝑒𝑡

𝐾

𝑘=1

≤ 𝑓3𝑗𝑒 (

∀𝑗 = 1,2, … , 𝑛; ∀𝑒 = 1,2, … , 𝑛 𝑎𝑛𝑑 ∀𝑡 = 1,2, . . , 𝑛

) (42)

𝑋1𝑗𝑖𝑒𝑡, 𝑋2𝑗𝑖𝑒𝑡, 𝑋3𝑗𝑖𝑒𝑡 ≥ 0; Integer (∀𝑗 =, … , 𝑛; ∀𝑖 = 1, … , 𝑛;

∀𝑒 = 1, … , 𝑛 𝑎𝑛𝑑 ∀𝑡 = 1, . . , 𝑛 )

(43)

𝑋4𝑗𝑘𝑒𝑡, 𝑋5𝑗𝑘𝑒𝑡 ≥ 0; Integer (∀𝑗 =, … , 𝑛; ∀𝑘 = 1, … , 𝑛;

∀𝑒 = 1, … , 𝑛 𝑎𝑛𝑑 ∀𝑡 = 1, . . , 𝑛 )

(44)

𝑍𝑖𝑘𝑒𝑡, 𝑍1𝑘𝑑𝑒𝑡, 𝑍2𝑖𝑑𝑒𝑡 ≥ 0; Integer (∀𝑖 =, … , 𝑛; ∀𝑘 = 1, … , 𝑛; ∀𝑑 = 1, … , 𝑛;

∀𝑒 = 1, … , 𝑛 𝑎𝑛𝑑 ∀𝑡 = 1, . . , 𝑛 )

(45)

𝑡𝑚𝑖𝑘𝑖𝑘𝑡, 𝑡𝑚𝑖𝑑𝑖𝑑𝑡 , 𝑡𝑚𝑖𝑘𝑘𝑑𝑡 ≥ 0 (∀𝑖 = 1, … , 𝑛; ∀𝑘 = 1, … , 𝑛; ∀𝑑 = 1, … , 𝑛

𝑎𝑛𝑑 ∀𝑡 = 1, . . , 𝑛 ) (46)

𝜇𝑘 = (0,1) (∀𝑘 = 1,2, … , 𝑛) (47)

𝑄𝑘𝑡, 𝑔𝑑𝑝𝑚𝑘𝑝𝑡 ≥ 0 (∀𝑘 = 1, … , 𝑛; ∀𝑝 = 1, … , 𝑛

𝑎𝑛𝑑 ∀𝑡 = 1, . . , 𝑛 )

(48)

𝑎𝑖𝑡 , 𝑎1𝑖𝑡 ≥ 0 (∀𝑖 = 1,2, … , 𝑛); ∀𝑡 = 1,2, . . , 𝑛) (49)

Eqs (18)-(19) try to guarantee that total assigned loader capacity is enough to load

debris at total quantity of assigned trucks.

Eq (20) tries to guarantee that sum of demolished quantity of buildings for all time

period equal to number of destroyed buildings at each DAS.

Eq (21) tries to guarantee that total capacity of assigned vehicles enough to demolish

the destroyed buildings at each DAS.

Eqs (22)-(26) impose constraints on the amounts demolished buildings at DAS,

transported from DAS to TSA and TSA to disposal site, recycled and inventory at TSA

in the first time period.

8

Eq (27) is the balance constraint between demolished amount and number of destroyed

buildings at each DAS for each time period. The demolished amount is assumed as

debris amount at the next time period.

Eq (28) concerns inventory of debris at first time period.

Eq (29) tries to guarantee that total number of assigned trucks must be enough to

transport debris from DAS to TSA. It is assumed that transported amount of debris from

DAS will add the inventory of the TSA at the next time period.

Eq (30) tries to guarantee that total transported amount of debris from DAS is equal to

sum of initial amount of debris and numbered of destroyed buildings amounts.

Eq (31) is the balance constraint between demolished, transported amount and inventory

of debris at each DAS for each time period.

Eq (32) tries to guarantee that total number of assigned trucks must be enough to

transport debris from TSA to disposal sites. It is assumed that transported amount of

debris from TSA will add the inventory of the disposal site at the next time period.

Eq (33) defines the recycled amounts for the use where the ratio of amount of recycled

material to the transported amount of debrid from DAS to TSA.

Eq (34) tries to guarantee that total transported amount of debris from TSA to disposal

site is equal to sum of recycled amount of debris for each time period.

Eq (35) is the balance constraint at inventory of debris at each TSA for each time period

considering transported debris from DAS to TSA and TSA to disposal site and recycled

amount.

Eqs (36)-(37) try to guarantee that inventory at TSA and transported amount of debris

from DAS to TSA for each time period must not exceed the capacity of TSA.

Eqs (38)-(39) try to guarantee that total number of transported vehicles must equal to

total number of assigned trucks from source of vehicle j to DAS i or TSA k for each

time period.

9

Eqs (40)-(42) try to guarantee that number of assigned vehicles does not exceed the

number of vehicles at vehicles sources. It is assumed that all assigned vehicles come

back from DAS and TSA to source of vehicles at the beginning of each time period.

Eqs (43)-(45) try to guarantee that these decision variables are non-negative and integer.

Eq (46 tries to guarantee that transported amount decision variables are non-negative.

Eq (47) tries to guarantee that opening TSA decision variable is binary.

Eqs (48)-(49) try to guarantee that these decision variables are non-negative.

3.3. Modeling Challenges

In this thesis, some difficulties are encountered due to various reasons during the

modelling process of the debris removal problem. The encountered difficulties as are

summarized below:

The most important challenge encountered is that being a limited number of

establishments for debris recycling operation in Turkey. The used data on the

model were collected from İSTAÇ which is the biggest and experienced

establishments for this subject in Turkey.

Disaster management efforts made a few years in Turkey and there is

insufficient information and data in this regard. Even if some types of data are

accessed easily accessing previous data on this case was very difficult.

Arc and node routing problem is studied for debris removal problem and

recycling issue is not dealt with any of the paper. There have been difficulties in

finding similar researches. So it has become difficult to adapt international

examples to Turkey.

Determining weights of objective function were very hard due to the small

number of authorized persons can get suggestions to determine weights.

10

3.4. Estimation of Input Data

The proposed methodology is applied in numerical example for Sakarya Case.

Assumptions and acceptances of the model were determined using data of previous

disasters and the consideration of authorities’ suggestions and data were obtained from

the institutions working on this issue and articles.

Three objective functions are weighted according to the consideration of authorities’

suggestions. Weights of objective functions are shown on Table 3.4.1.

Table 3.4.1. Weights of objective functions

Weight

Total cost 0,65

Negative impact on victims 0,3

Emission index of vehicles 0,05

According to AFAD, 66.441 homes and 10.901 workplaces were heavily damaged,

67.242 homes and 9.927 workplaces were moderately damaged, 80.160 homes and

9.712 workplaces were slightly damaged by 1999 Marmara earthquake in sakarya

(Gökçe and Tetik, 2012). Data of demolished and heavily damaged buildings are

estimated with using 1999 Marmara earthquake data and the amount of homes from

Turkish Statistical Institute (TUİK) which is changed by considering new districts of

Sakarya such as Arifiye, Erenler, Serdivan. The used data for demolished, heavily

damaged buildings and 2015 population ratio of disaster affected places which is found

from internet are seen on Table 3.4.7. are seen in Table 3.4.2. The DASs , TSAs,

location of vehicles, disposal sites and their relations are displayed in Figure 3.4.1.

The data of vehicles’ locations are hypothesized based on the location of governmental

agencies and excavation companies at Sakarya. TSA (k) are hypothesized considering

empty fields. According to the information received from authorized people two

disposal areas (d) are used in the model. While distances between 16 districts of

Sakarya (i) and location of vehicles or temporary storage area, It is assumed that center

of the damage was the midpoint of the districts. The data of distances between locations

of vehicles and disaster affected areas (dji), temporary storage areas and disposal areas

(d1kd), disaster affected areas and temporary storage areas (d2ik), disaster affected areas

11

and disposal areas (d3id), locations of vehicles and temporary storage areas (d4jk) are

estimated with using Google Maps. The tables of the data of distances are seen in

Appendix 3,4,5,6.

Table 3.4.2. Quantity of demolished and heavily damaged buildings

Districts

Quantity of

demolished

buildings (aai)

Quantity of

damaged

buildings (aa1i)

Population Population

Rate

Pamukova i1 145,65 1141,6 28506 0,03

Taraklı i2 107,95 387,8 6991 0,01

Kocaali i3 546,2 1312,6 21050 0,02

Hendek i4 9,1 2756,2 78179 0,08

Geyve i5 811,5 1925,4 48374 0,05

Sapanca i6 3264,8 4897,2 39686 0,04

Söğütlü i7 6,1 622 13960 0,01

Akyazı i8 141,45 3348,4 85499 0,09

Kaynarca i9 50 400 23489 0,02

Ferizli i10 130 500 24834 0,03

Karapürçek i11 500 700 12381 0,01

Adapazarı i12 10000 18300 269079 0,28

Karasu i13 200 1300 59130 0,06

Arifiye i14 4200 3150 39632 0,04

Serdivan i15 7300 8250 120731 0,13

Erenler i16 4700 5245 81660 0,09

Total 32112,75 54236,2 953181 1

12

Figure 3.4.1. DASs , TSAs, location of vehicles, disposal sites and their relations

The vehicles capacity (c1, c2, c3) and cost (n1, n2, n3) of demolishing, loading and

transportation data are found on internet are assumed as standard for every type of

vehicles. The data of them is seen in Table 3.4.3.

The data of the quantity of vehicles (f1, f2, f3) are assumed for governmental agencies

and found on internet for excavation companies. The tables of the data of vehicle

quantities are seen in Table 3.4.4.

13

Table 3.4.3. Capacity of vehicles and cost of work

Type of

Vehicles

Capacity of Vehicles

(m3 x 66 days)

Cost of work

(TL x 66 days)

Excavator 62,04 10454

Loader 237,6 10454

Truck 1056 7920

Table 3.4.4. Quantity of excavator, loader and truck at location of vehicle j

QUANTITY EMISSION INDEX

OF EXCAVATOR

EMISSION INDEX

OF LOADER

EMISSION INDEX

OF TRUCK

VE

HIC

LE

LO

CA

TIO

N A

RE

A

e0 e1 e2 e3 e4 e0 e1 e2 e3 e4 e0 e1 e2 e3 e4

j1 0 2 3 0 2 0 4 5 4 2 0 9 0 6 0

j2 3 7 0 0 0 5 11 6 3 0 5 11 6 3 0

j3 4 7 0 2 0 9 17 6 3 0 10 24 11 5 0

j4 0 2 1 0 0 0 10 3 0 0 0 10 3 0 0

j5 11 14 5 0 0 15 21 7 5 2 19 24 12 10 0

j6 7 12 0 0 0 18 26 3 3 0 18 40 8 5 0

j7 7 4 0 0 0 10 110 10 0 0 10 10 10 0 0

j8 4 10 0 0 0 3 12 10 0 0 5 18 15 0 0

j9 2 2 0 0 0 5 5 15 0 0 8 2 15 0 0

j10 2 11 0 0 0 10 10 0 0 0 12 13 0 0 0

j11 7 3 4 0 0 5 15 5 0 0 5 19 6 0 0

j12 8 7 2 0 0 5 13 2 0 0 5 23 2 0 0

j13 8 5 0 0 0 20 15 0 0 0 22 18 0 0 0

j14 7 9 9 3 0 12 20 11 2 0 16 30 20 4 0

j15 7 8 5 0 0 10 20 10 0 0 14 24 12 0 0

j16 3 12 5 0 0 5 18 2 0 0 7 31 4 0 0

j17 4 10 7 0 0 4 11 5 0 0 6 16 8 0 0

j18 9 8 2 0 0 10 20 5 0 0 14 22 5 0 0

j19 0 5 2 1 1 0 1 2 1 2 0 11 8 12 1

j20 0 2 8 0 0 0 3 2 3 0 0 0 0 19 2

j21 0 6 2 0 0 0 2 2 0 0 0 5 8 4 0

One t period is assumed as three months. So the capacity of vehicles is assumed equal

and found by multiplying with 66 days. Fuel cost of vehicles (B) is assumed as 10560

TL/3 months is found calculating the average of the amount of fuel (20 TL/hour).

Capacity of temporary storage areas (ck) are assumed based on the maximum three

months (t period) production capacity of the recycling machines is 24000 m3/3 months.

14

The opening cost of temporary storage areas (𝜑) are assumed as 1774800 TL

considering to be bought two screening and a crushing machines for each one of them.

The data of emission penalty of e emission index (ed) is assumed based on a report

published by the truck manufacturers are given in the following Figure.3.4.1.

Figure 3.4.2. Penalty of e emission index

The sales prices of recycled materials (g) are assumed based on internet price for iron,

İSTAÇ data for aggregate. The sales prices of recycled materials are seen in Table 3.4.5.

Table 3.4.5. The sales prices of recycled materials

PRICE/m3 Iron

0-12 mm

aggregate

13-22 mm

aggregate

23-38 mm

aggregate

Price 600 1,5 2 2

The cost of recycling process (𝜑1) is assumed as 0,56 TL/m3 based on the information

received from İSTAÇ.

The recycling rates of debris are assumed based on the information of the average

amount of recycling debris received from İSTAÇ. It is seen in Table 3.4.6.

Euro 6

Euro 5 Euro 4

Euro 2

Euro 3

Euro 1

1 2 3 4 5 6 8 7 -95 %

-97

%

0,10

0,20

0,30

0,40

NOx (g/kWh)

PM (g/kWh)

ede Euro 1 1993 0,35

Euro 2 1996 0,15

Euro 3 2000 0,10

Euro 4 2006 0,05 Euro 5 2009 0,05

Euro 6 2014 0,02

15

Table 3.4.6. The recycling rates of debris

Iron 0-12 mm

aggregate

13-22 mm

aggregate

23-38 mm

aggregate

Rate of

products 0,15 0,12 0,31 0,02

The negative impact rate on disaster affected areas for every time period (nirate) is

assumed based on study of Hu and Sheu, 2013 as 150t.

Labor cost for governmental agencies is assumed based on Minimum Wages in Turkey

with effect from 01-01-2016 to 31-12-2016 as 1300 TL/a month. Labor cost of 3 month

time period is 3900 TL/3 months. Labor cost for excavation companies is assumed

considering cost of service, 1000 TL/a month, as 3900 + 3000 = 6900 TL/3 months.

3.4.1. Test data set

Because of the proposed input data set’s solution time, a small data set is used to test the

model. The test data set has 5 DASs, 2 source of vehicles, 2 TSAs, 2 disposal areas, 3

time periods, 2 process types, 2 emission index. The data of emission penalty of e

emission index (ed) is assumed as 0.05 and 0.35. The amount of debris, the numbers of

demolished buildings and population rates are shown in Table 3.4.1.1.

Table 3.4.1.1. The amount of debris, the numbers of demolished buildings and

population rates

Quantity of

demolished

buildings (aai)

Quantity of

damaged

buildings (aa1i)

Population Rate

(nei)

i1 146 1141 0.04

i2 108 387 0.01

i3 547 1312 0.014

i4 9 2756 0.016

i5 812 1925 0.02

The distances between locations are seen in Table 3.4.1.2.

16

Table 3.4.1.2. The distances between locations

i1 i2 i3 i4 i5 k1 k2

j1 75.5 15.4 30.4 20.4 33.2 12.3 32.5

j2 42.5 7.59 64.4 2.13 24.6 2.7 65.3

k1 44.1 17.3 34.3 7.3 66 x x

k2 2.3 15.4 5.4 65.4 12.4 x x

d1 x x x x x 7.3 31.4

d2 x x x x x 17.3 15.4

The number of vehicles at source of vehicle locations is seen in Table 3.4.1.3.

Table 3.4.1.3. Test set’s quantity of excavator, loader and truck at location of vehicle j

EMISSION INDEX OF

EXCAVATOR

EMISSION INDEX OF

LOADER

EMISSION INDEX OF

TRUCK

e0 e1 e0 e1 e0 e1

j1 10 12 10 15 10 15

j2 10 12000 10 15000 8 20000

The recycling rates of debris and their prices are assumed as 0.15; 600 TL iron and 0.12;

1.5 TL 0-22 mm aggregate. The vehicle usage cost Rje are assumed as 10 TL and 6 TL

for public agencies, 1000 TL and 600 TL for private agencies.

3.5. Results and Analysis

The proposed model is run using input data which is mentioned at section 3.4 for 23

hours but a solution could not be obtained because of the existence large number of

integer variables on the other hand a feasible solution is obtained which is seen at the

Lingo screen which is seen at figure 3.5.1. So a smaller data set which has 5 DASs, 2

source of vehicles, 2 TSAs, 2 disposal areas, 3 time periods, 2 process types, 2 emission

index has been decided to use in order to get a solution.

After determination of the preference ranges of the objective functions, weights are

determined by using LPP technique. For the LPP technique considering the individually

results of objective functions, the preference ranges are decided as in Table 3.5.1. LPP

17

weight algorithm is run by using data shown in Table 3.5.1. When weights are

normalized and show in Table 3.5.2.

Figure 3.5.1. Lingo screen of the proposed model by using input data

Table 3.5.1. The preference ranges of the case study

OF1

Class 1S (min.)

OF2

Class 1S (min.)

OF3

Class 1S (min.)

Ideal range < 1500000 < 35000 < 850

Desirable range 1500000 - 2500000 35000 - 55000 850 - 1200

Tolerable range 2500000 - 3500000 55000 - 75000 1200 - 1500

Undesirable range 3500000 - 4000000 75000 - 95000 1500 - 2500

Highly Undesirable range 4500000 - 5000000 95000 - 105000 2500 - 3000

Unacceptable range < 5000000 < 105000 <3000

Table 3.5.3. Normalized weight deviations of objectives

�̌�12+ �̌�13

+ �̌�14+ �̌�15

+

OF1 0.1 0.121 0.736 0.043

�̌�22+ �̌�23

+ �̌�24+ �̌�25

+

OF2 0.245 0.061 0.074 0.62

�̌�32+ �̌�33

+ �̌�34+ �̌�35

+

OF3 0.047 0.025 0.025 0.903

18

The results of net total cost, the negative impact of the debris on affected people and

emission effect are obtained and seen in Table 3.5.4 when the proposed model is run by

using LPP technique.

Table 3.5.4. The numerical analysis

OF1 OF2 OF3

Numerical Result 1489019 35000 847.2020

The preference ranges Ideal range Ideal range Ideal range

Considering numerical analysis, all objective functions are in ideal ranges. As a result of

the LPP solution, number of assigned vehicles is shown in Table 3.5.5.

Table 3.5.5. Quantity of assigned excavator, loader and truck from location of

vehicle j to DAS i and TSA k

QUANTITY OF

EXCAVATOR

QUANTITY OF

LOADER

QUANTITY OF

TRUCK

j1 j2 j1 j2 j1 j2

i1 0 19 0 9 2 0

i2 3 4 0 5 0 1

i3 7 15 10 0 2 0

i4 0 45 0 14 0 3

i5 10 22 0 14 3 0

k1 x x 0 14 0 3

k2 x x 14 9 5 0

Total 20 105 24 65 12 7

Total

Vehicle

at j

22 12010 25 15010 25 20008

19

4. CONCLUSIONS

Disaster operation plan has a vital importance in reducing the effect of disaster on

human beings, natural environment, national economy and social structures. For the

disaster situation, the activities which reduce the negative impacts of disaster on

affected people, nature and economy as important as response and complete times of the

operations which are the most important factors. In this regard, debris removal

operations are becoming increasingly important.

In this thesis, vehicle allocation problem was handled considering the debris collection,

transportation and recycling operations. This model is a mixed integer programming

model. A model which has three objective functions as (i) minimization of operation

and decision net total costs, (ii) minimization of negative impacts of debris on people

and (iii) minimization of emission indexes of assigned vehicles is proposed for this

problem. LPP technique is used to integrate three objective functions into one objective

function. By employing this approach, typically complex texture of a decision maker’s

preferences can be satisfied. The weight of preference degrees for each performance

criteria is measured by using LPP technique which gives an advantage to remove the

essential to choose weights needed by some decision support tools. Lingo software is

used to run the proposed model. Because of the solution time of the proposed model’s

large data set, the smallest data set is used to obtain a solution. Considering numerical

analysis of test model, all objective functions are in ideal ranges.

Proposed methodology aims help the authorities, who work for this problem, in order to

conduct debris removal operations as quickly as possible considering total cost and

natural effects of debris and assigned vehicles.

For future research, firstly, Heuristic techniques such as Tabu Search, Genetic

Algorithms, simulating analyzing, etc. are proposed to run the proposed model for large

data set. Secondly, proposed model do not have decision of transportation of debris

from DAS to disposal site and it can be added so the proposed model might be

improved by adding the new criterion like maximum amount of recycled debris and new

constraints.

20

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25

APPENDIX

Appendix1: The classification of studies in disaster management and humanitarian logistics issues

Matematiksel Model

Author,

Date

Category

(Phase) of

Disaster

Topic of Study The objective functions Consideration of

Uncertainty Model Type Used Technique

Selection

Evacuatio

n Studies

Conceptional

Studies

Peeta et al.,

2010

Pre-

(Preparation)

Investment decisions to

strengthen the road

network

Expectations of the shortest path cost minimization Possibility of road

damage

Stochastic

programming 2-stage stochastic programming _ _

Görmez et

al., 2011

Pre-

(Preparation)

Facility location selection

(Location of storage

selection)

The first stage model:

The minimization of the distance which is weighted by

demand

The second stage model:

The first objective function:

The minimization of the average travel distance of victims

The second objective function:

minimization of the number of newly built facility

Scenario analysis

The first stage model:

Integer programming

The second stage

model:

Binary (0,1) Integer

programming

ε- constraint method _ _

Noyan, 2012 Pre-

(Preparation)

Determination the facility

location and the level of

inventory

The first stage model:

The cost minimization

The second stage model:

The cost minimization

The level of destruction in

demand and transport

network

Stochastic

programming

Two stage average-risk stochastic

programming _ _

Özgüven

and Özbay,

2013

Pre-

(Preparation)

Determination of

inventory level of

emergency needs

The cost minimization Inventory levels, demand Stochastic inventory

control model

p-level effective points (pLEPs)

method + Prѐkopa-Vizvari- Badics

algorithm

_

Usage of

RFID

technology

Salman and

Yücel, 2014

Pre-

(Preparation)

Facility location selection

(state of network

connection error)

Maximization of demand levels are met probabilities of different

scenarios occurrence

Stochastic integer

programming

Tabu search algorithm _ _

Kılcı et al.,

2015

Pre-

(Preparation)

Determination the

location of shelter

Maximization of the weight of the smallest weighted

shelter space

scenario analysis for

different parameter values

Mixed integer linear

programming Robust optimization _ _

Akgün et al.,

2015

Pre-

(Preparation) Facility location selection

Minimizing the risk (p-centered risk model);

Minimizing the maximum distance between the demand

point and the closest facility with it (p-centered model)

Fault decision tree

analysis for risks of

demand; scenario analysis

for different p values

Linearized integer

model;

p-centered risk model

(for comparison)

Fault tree analysis (for measuring the

resistance of demand points);

Solving methodology as linearization

and a linear integer program

_ _

26

Appendix1(Continued): The classification of studies in disaster management and humanitarian logistics issues

Matematiksel Model

Author, Date

Category

(Phase) of

Disaster

Topic of Study The objective functions Consideration of

Uncertainty Model Type Used Technique

Selection

Evacuatio

n Studies

Concep

tional

Studies

Üstün and

Anagün, 2015

Pre-

(Preparation) Mitigation budget allocation

The first objective function:

Maximizing the NPV of financial earnings from

mitigation investments

The Second objective function:

Maximization of the number of the

strengthening buildings

The third objective function:

Maximizing the investment of strengthen

certain strategically

important buildings and regions

_ Linear models;

Mixed integer models

Weighted sum method (Linear model);

Epsilon constraint method (Mixed integer

models);

Data envelopment analysis (for comparison of

Pareto efficient solution )

_ _

Barbarosoğlu

et al., 2002

After

(Response)

Top-level decisions:

Identifying the helicopter

fleet, the pilot assignments

and determination the

number of rounds

Lower level decisions:

routing and loading analysis

Top-level decision model:

Minimizing the cost of helicopters and pilots

Lower level decisions model:

Minimizing the service completion time

_

Top-level decision

model:

Integer programming

Lower level

decisions model:

Mixed Integer

Programming

Heuristic approach for coordination of

upper and lower level

multiple-

criteria

decision

analysis to

compare

alternative

solution

_

Barbarosoğlu

and Arda,

2004

After

(Response)

Transport situation in the

event of disaster response

The first stage model:

Minimization of total first stage transportation

costs and the costs of the expected relief

supplies

The second stage model:

Minimization of penalties costs associated with

the total flow, changing modes, holding

inventory and lack of inventory

uncertainty caused by the

damage to the transportation

systems are considered with

improved scenario analysis for

the supply parameters

Multi-product and

multi-modal network

flow model:

stochastic linear

programming

Stochastic linear programming approach _ _

Yi and

Özdamar,

2007

After

(Response)

logistics coordination model

for support and evacuation Minimization of service delays

Scenario analysis for Different

nodes, location and number of

vehicles

Mixed integer and

multi-product

network flow model

The first stage model:

Solution of mixed integer multi-product model

The second stage model:

Developing the vehicle routes and loading /

unloading instructions

_ _

27

Appendix1(Continued): The classification of studies in disaster management and humanitarian logistics issues

Matematiksel Model

Author, Date

Category

(Phase) of

Disaster

Topic of Study The objective functions Consideration of Uncertainty Model Type Used Technique

Selection

Evacuation

Studies

Concep

tional

Studies

Arşık and

Salman, 2013

After

(Response)

Modelling the

vulnerability of the

road network after an

earthquake

Route-based accessibility criteria Examined the possibility of

remain networks unclosed

Modelling the probability of

remaining durable the access roads

within the network

Statistical analysis _ _

Salman and

Gül, 2014

After

(Response)

Capacity allocation,

distribution emergency

needs and

providing medical

assistance service

Mixed integer model:

Minimizing the total time of transportation of relief supplies

and the total waiting time

Transportation and selection of location model:

Minimizing the costs of total emergency need's

transportation, total waited time and opening the field

hospital

scenario analysis for different

situations of parameters such os

patients arrival, number of

periods and transportation time

Mixed integer models (Dynamic

model of needs transportation)

+

Integer models

Hierarchical analysis

approach _ _

Aksu and

Ozdamar,

2014

After

(Response)

Determination of closed

roads and clearing them

with limited resources

(accessibility and

Evacuation)

The first stage model:

Maximization of the weighted earliest completion time for

the restoration of the route

The second stage model:

Maximization of the total early services for all regions in the

disaster areas

Modified the second stage model:

Minimization of the maximum difference between the two

early values of any region

Equipment availability analysis

for different scenarios

Integer programming (debris

removal scheduling model);

0-1 integer programming model

(equipment allocation model);

Modified integer programming

model (equipment allocation

model)

Integer programming _ _

Özdamar et

al., 2014

After

(Response)

analysis of existing and

potential residential

area

The first objective function:

Minimization of cumulative scale of

the failure to reach situation

The second objective function:

Minimization of completion time

Scenario analysis based on the

number of closure rate of

different roads and different

cleaning equipment

Iterative mixed integer modeling Heuristic _ _

Sahin et al.,

2015

After

(Response)

Debris removal during

disaster response period

Minimization of the total travel effort (transportation and

debris removal) were spent to clean the debris from closed

roads

Reducing solution time by

increasing the size of the

network increases

Arc and node routing (general

routing) model Heuristic _ _

28

Appendix 1(Continued): The classification of studies in disaster management and humanitarian logistics issues

Matematiksel Model

Author, Date

Category

(Phase) of

Disaster

Topic of Study The objective functions Consideration

of Uncertainty Model Type Used Technique Selection Evacuation Studies

Concep

tional

Studies

Onan et al.,

2015

After

(Recovery)

Determining the

location of the

temporary storage

center with the model

which include plans

of collecting and

transportation of waste

in order to find

environmentally

sustainable way

The first stage model:

The model for determining the location of temporary storage

centers

The first objective function:

Minimization of total weighted average distance to the point

source of waste in temporary storage

The second objective function:

minimization of the total population of cells including

temporary storage area

The second stage model:

Location determination and allocation model

The first objective function:

Minimizing the cost of opening the temporary storage area

and waste transportation for the temporary storage

The second objective function:

Minimization of population who exposed to the risk posed by

recycling and sorting plant

_ Multi-objective integer

programming model

2-stage multi-objective

optimization (NSGA-II) _ _

Alparslan et

al., 2008

Pre-

(Preparatio

n)

A geographic

information system

model for residential

suitability in order to

reduce the impact of

disasters

_ _ _ _

GIS model (distance from the

main benefit, ground

acceleration, geological soil type

and the slope of the land ):

to be investigated alternative of

existing settlements and new

settlements neighborhood of Bolu

in terms of earthquake resistance

_

29

Appendix 2: The Conceptional Studies

Author,

Date

Category

(Phase) of

Disaster

Conceptional Studies

Börühan et

al., 2012

Pre-

(Preparation)

The importance of management of logistics planning and

checklist for disaster management

Caymaz et

al., 2013

Pre-

(Preparation) Disaster crisis management

Ersoy ve

Börühan,

2013

Pre-

(Preparation) The importance of disaster logistics in terms of logistics

processes

Tanyaş et

al., 2014

Pre-

(Preparation) New Model for Disaster Logistics Management at Rize, Turkey

Kaynak and

Tuğer, 2014

Pre-

(Preparation)

Coordination and collaboration functions of disaster

coordination centers for humanitarian logistics

Jahre et al.,,

2015

Pre-

(Preparation)

Determining the needs of the supply chain by examining the

example of 3 different earthquakes in the past

Güzey, 2015

Pre-

(Preparation)

The earthquake risk of the area at risk for Disaster and urban

development and urban renewal policies in Ankara were

investigated taking into consideration the recent legislation

concerning the area at risk for Disaster.

Ozkazanc ve

Yuksel,

2015

Pre-

(Preparation)

Evaluation of disaster awareness and sensitivity levels of tertiary

students were examined. Date for this project was obtained with

questionnaire data collection methods from the students who

study at Gazi University Faculty of Architecture, Urban and

Regional Planning program. The need to reduce disaster losses

in education have been revealed with this Project.

Koçak vd.,

2015

Pre-

(Preparation)

Against disasters, preparations of individual behavior of

emergency medical services personnel are determined. In the

study, Mann-Whitney and Kruskal-Wallis and chi-square

statistical techniques were used. In conclusion, no matter at what

level of education employees have, It has been decided that they

should receive training for disasters and emergencies.

Albayrak

vd., 2015

Pre-

(Preparation)

In this study, Rapid seismic risk assessment was conducted for

the existing building stock in urban areas. Proposed

methodology for evaluation of seismic failure risk in urban

buildings' stock was developed by SUCUOĞLU is based on the

screening procedure technique. Age of the building, the number

of disasters, the presence of small incident, short column, heavy

setback and pounding effect was used for risk assessment.

30

Appendix 3: Distances between vehicle location area j to disaster affected area i

DISTANCE (km)

AFFECTED AREA

i1 i2 i3 i4 i5 i6 i7 i8 i9 i10 i11 i12 i13 i14 i15 i16

VE

HIC

LE

LO

CA

TIO

N A

RE

A

j1 42,5 64,4 73 35 32,4 17,6 22,3 30,8 33,9 26,7 24,5 8,8 55,7 10,7 7 4,3

j2 43,5 65,4 75,1 37,5 35,2 16,1 24,4 33,3 34,8 28,8 27 10,8 57,8 11,7 5,6 6,9

j3 45,8 67,7 73,7 40,2 37,5 18,8 23 36,1 33,3 27,4 29,8 9,4 56,4 14 4,7 8,2

j4 45,3 67,3 71,5 37,2 37,1 19,5 28,8 33 31,3 25,2 26,8 7,2 54,2 13,5 5,5 6,6

j5 43,2 65,2 70 35,3 35 19,6 19,3 31,2 33,3 23,6 24,9 7 52,6 11,4 9 4,6

j6 43 64,9 71,9 35 34,7 19,5 21,2 30,9 33,5 25,6 24,6 7,2 54,6 11,2 8,5 4,7

j7 40,1 62 74,2 31,9 31,8 16,6 23,5 27,7 40,3 27,9 21,4 14 56,9 8,3 8,8 1,6

j8 47,8 0,18 137 94,7 33,6 62 86,5 85,4 99,5 90,9 88,3 74 120 56,5 70,4 65,1

j9 53,5 75,4 72,8 45,5 45,2 27,6 21,3 41,3 23,6 26,5 34,9 13,2 55,5 21,7 13,7 14,7

j10 40,1 62 74,6 34,3 31,8 16,7 23,9 30,1 37,1 28,3 23,8 11,9 57,3 8,3 5,4 3,6

j11 0,26 47,1 114 71,5 14,2 38,9 63,1 62,2 76,4 67,5 65,2 50,9 96,5 33,3 47,2 42,1

j12 72,4 94,3 47,7 0,35 60,6 45,1 47,1 22,7 63,9 51,5 32,4 37,6 80,5 37 35,8 30,7

j13 66,2 88,1 53,2 5,5 57,9 46,3 50,4 23,1 67,2 54,8 28,3 40,9 83,8 34,4 37,7 34

j14 61,9 83,8 94,4 23,9 53,6 41,7 43,6 0,7 60,4 48 10 34,1 77 29,2 32,4 27,3

j15 41,2 63,1 75,2 28,9 32,9 17,5 24,5 24,7 41,2 28,8 18,5 14,9 57,8 9,4 8,2 3,5

j16 96,2 118 17,5 80,4 88 72,5 34,1 76,3 41,7 29,9 74,5 44,6 0,28 64,4 59,2 55,2

j17 46,4 68,3 84,7 40,4 38,2 18,3 29,9 36,2 40,3 34,3 30 16,4 67,4 14,6 11,1 12,6

j18 47,4 69,4 79,2 41,4 39,2 19,4 28,5 37,3 38,9 32,9 31 15 61,9 15,6 7,8 13,6

j19 42,8 64,8 72,9 37 34,6 17,8 22,2 32,9 33,1 26,6 26,6 8,6 55,6 11 5,6 11,6

j20 43,5 65,4 70 35,3 35,2 25 19,3 33,5 32,9 23,7 27,1 6,6 52,7 11,7 8,3 12,1

j21 43 64,9 72 35,1 34,8 19,6 21,3 30,9 33,4 25,7 24,6 7,1 54,7 11,2 8,3 9,7

31

Appendix 4: Distances between vehicle location area j to temporary storage area k

DISTANCE (km) DISPOSAL AREA

d1 d2

TE

MP

OR

AR

Y

ST

OR

AG

E

AR

EA

k1 17,3 11,4

k2 11,9 18,9

k3 17,6 21

k4 14,7 18,9

Appendix 5: Distances between disaster affected area i to temporary storage area k and

disposal area d

DISTANCE

(km)

TEMPORARY STORAGE AREA DISPOSAL AREA

k1 k2 k3 k4 d1 d2

AF

FE

CT

ED

AR

EA

i1 44,1 38,7 44,4 41,6 31,1 57,1

i2 66 60,6 66,3 63,5 53,1 79

i3 72,8 76,2 78,4 79,8 87,4 61,7

i4 38,5 32,2 27,9 35,5 43,1 41,3

i5 35,8 30,4 36,1 33,3 24 50

i6 18,5 16,2 20,7 10,4 11,9 29,9

i7 22,1 25,5 27,7 29,1 36,7 10,2

i8 34,3 28 23,8 31,4 38,9 37,2

i9 32,5 42,3 44,4 40 49,2 29,1

i10 26,5 29,9 32,1 33,5 41,1 15,4

i11 29,5 21,8 19,1 25,1 32,6 35,4

i12 8,5 16 18,1 16,1 27,2 7,4

i13 55,5 58,9 61,1 62,5 70,1 44,4

i14 14,1 6,9 12,6 6,4 6,6 25,3

i15 4,6 13 17 10,8 20,5 15,9

i16 6,4 6,8 0,65 10,2 17,7 17,3

32

Appendix 6: Distances between vehicle location area j to temporary storage area k

DISTANCE (km) TEMPORARY STORAGE AREA

VE

HIC

LE

LO

CA

TIO

N A

RE

A

k1 k2 k3 k4

j1 2,7 8,4 10,5 8,7

j2 3,1 7,6 11,6 5,4

j3 1,6 9,1 13 6,8

j4 2,1 11,8 15,8 9,6

j5 2,8 8,9 11,1 9,5

j6 3,6 10,6 11,2 9,6

j7 4,7 5,5 7,6 6,3

j8 65,8 60,5 66,2 63,3

j9 9,4 19,2 20,8 17

j10 4 7,4 9,4 7,6

j11 44,5 39,1 44,8 42

j12 38,4 32,1 27,8 35,5

j13 39,1 34,4 28,5 37,8

j14 34,9 28,6 24,3 31,9

j15 12,7 5 3,4 8,3

j16 55,2 58,6 60,8 62,2

j17 8,5 11,2 15,7 7,7

j18 7,1 12,5 17 8,9

j19 1,4 8,5 16,1 7,4

j20 3,1 11,2 12,9 9,7

j21 3 8,7 14,9 9,2

33

Appendix 7: Quantity of excavator, loader and truck at location of vehicle j

QUANTITY EMISSION INDEX

OF EXCAVATOR

EMISSION INDEX

OF LOADER

EMISSION INDEX

OF TRUCK

VE

HIC

LE

LO

CA

TIO

N A

RE

A

e0 e1 e2 e3 e4 e0 e1 e2 e3 e4 e0 e1 e2 e3 e4

j1 0 2 3 0 2 0 4 5 4 2 0 9 0 6 0

j2 3 7 0 0 0 5 11 6 3 0 5 11 6 3 0

j3 4 7 0 2 0 9 17 6 3 0 10 24 11 5 0

j4 0 2 1 0 0 0 10 3 0 0 0 10 3 0 0

j5 11 14 5 0 0 15 21 7 5 2 19 24 12 10 0

j6 7 12 0 0 0 18 26 3 3 0 18 40 8 5 0

j7 7 4 0 0 0 10 11 10 0 0 10 10 10 0 0

j8 4 10 0 0 0 3 12 10 0 0 5 18 15 0 0

j9 2 2 0 0 0 5 5 15 0 0 8 2 15 0 0

j10 2 11 0 0 0 10 10 0 0 0 12 13 0 0 0

j11 7 3 4 0 0 5 15 5 0 0 5 19 6 0 0

j12 8 7 2 0 0 5 13 2 0 0 5 23 2 0 0

j13 8 5 0 0 0 20 15 0 0 0 22 18 0 0 0

j14 7 9 9 3 0 12 20 11 2 0 16 30 20 4 0

j15 7 8 5 0 0 10 20 10 0 0 14 24 12 0 0

j16 3 12 5 0 0 5 18 2 0 0 7 31 4 0 0

j17 4 10 7 0 0 4 11 5 0 0 6 16 8 0 0

j18 9 8 2 0 0 10 20 5 0 0 14 22 5 0 0

j19 0 5 2 1 1 0 1 2 1 2 0 11 8 12 1

j20 0 2 8 0 0 0 3 2 3 0 0 0 0 19 2

j21 0 6 2 0 0 0 2 2 0 0 0 5 8 4 0

Appendix 8: Nomanclature

Index

Index Explanation

i

j

k

t

p

d

e

Disaster-affected area

Vehicle location

Temporary storage area

Time period

Process type

Disposal area

Emission index

34

Appendix 8-continue: Nomanclature

Variables

Index Explanation

𝑋1𝑗𝑖𝑒𝑡 The amount of excavator with e emission index is assigned from vehicle

location j to disaster affected area i at time period t

𝑋2𝑗𝑖𝑒𝑡 The amount of loader with e emission index is assigned from vehicle

location j to disaster affected area i at time period t

𝑋3𝑗𝑖𝑒𝑡 The amount of truck with e emission index is assigned from vehicle location

j to disaster affected area i at time period t

𝑋4𝑗𝑘𝑒𝑡 The amount of loader with e emission index is assigned from vehicle

location j to temporary storage area k at time period t

𝑋5𝑗𝑘𝑒𝑡 The amount of truck with e emission index is assigned from vehicle location

j to temporary storage area k at time period t

𝑍𝑖𝑘𝑒𝑡 The amount of truck with e emission index transport the debris from affected

area i to temporary storage area k at time period t

𝑍1𝑘𝑑𝑒𝑡 The amount of truck with e emission index transport the debris from

temporary storage area k to disposal area d at time period t

𝜇𝑘 Decision of opening temporary storage area k (0;1)

𝑄𝑘𝑡 The amount of debris in temporary storage area k at time period t

𝑔𝑑𝑝𝑚𝑘𝑝𝑡 The amount of recycled p type debris in temporary storage area k at time

period t

𝑎𝑖𝑡 The amount of debris remaining in disaster affected area i at time period t

𝑎1𝑖𝑡 The destruction amount remaining in disaster affected area i at time period t

𝑡𝑚𝑖𝑘𝑖𝑘𝑡 Debris transported from to disaster affected area i to temporary storage area

k at time period t

𝑡𝑚𝑘𝑑𝑘𝑑𝑡 Debris transported from to temporary storage area k to disposal area d at

time period t

35

Appendix 8-continue: Nomanclature

Parameters

Index Explanation

𝑚𝑗𝑖 The cost of assigning process of vehicles from vehicle location k to

affected area i

𝑚1𝑗𝑘 The cost of assigning process of vehicles from vehicle location k to

affected area i

𝑓1𝑗𝑒 The amount of excavator with e emission index at vehicle location j

𝑓2𝑗𝑒 The amount of loader with e emission index at vehicle location j

𝑓3𝑗𝑒 The amount of truck with e emission index at vehicle location j

𝑐1 Capacity of each excavator in the vehicle location

𝑐2 Capacity of each loader in the vehicle location

𝑐3 Capacity of each truck in the vehicle location

𝑐𝑘𝑘 Capacity of temporary storage area k

𝑑1𝑘𝑑 Distances between temporary storage area k and disposal area d

𝑑2𝑖𝑘 Distances between disaster affected area i and temporary storage area k

𝑑3𝑗𝑖 Distances between vehicle location j and disaster affected area i

𝑑4𝑗𝑘 Distances between vehicle location j and temporary storage area k

𝑎𝑎𝑖 The estimated amount of debris at disaster affected area i

𝑎𝑎1𝑖 The estimated amount to be demolition at disaster affected area i

𝛿𝑘𝑝 Debris ratio for process p which can be done at temporary storage area k

𝑔𝑝 income derived from process p

𝜑𝑘 Cost of opening temporary storage area k

𝜑1𝑘 Recycling processing cost at temporary storage area k

36

Appendix 8-continue: Nomanclature

Parameters

Index Explanation

𝑛1 Demolishing cost

𝑛2 Loading / unloading cost

𝑛3 Transportation costs

𝑛𝑒𝑖 Population ratio of i location

𝑛𝑖𝑟𝑎𝑡𝑒𝑡 Negative impact rate on i location at t time

𝑒𝑑𝑒 Emission penalty of e emission index

𝑅𝑗𝑒 Vehicle usage cost has to e emission index in j vehicle location

B Fuel costs

37

PERSONAL INFORMATION

Name and Surname: Lamia Gülnur KASAP

Place of Birth: Sakarya

Date of Birth: 15.05.1989

Address: Bağlar Mah. Yavuz Selim Cad. No:36 ökkuşağı Apt. aire:2 Erenler/Sakarya

GSM: 5333729390

Mail: lamiagulnur@gmail.com

EDUCATION

2013- :Marmara University, Industrial Engineering, M.S.

2011-2012 Fall Semester: Institute of Technology Tralee, Ireland Erasmus program

2008-2013: Sakarya University, Industrial Engineering, B.Sc.

2003-2007: Sakarya Figen Sakallıoğlu Anatolian High School

WORK INFO

11.07.11-05.08.11: Temsa Global / Adapazarı Factory – Work Study Internship

2011-2012 Spring Semester: Divan Furniture/ Kuzuluk Factory/ Mattress line – Project

Study

06.08.12-05.09.12: Kordsa Global Turkey/ İzmit/ Kocaeli – Management and

Production Internship

2012-2013: Sakarya University, Engineering Faculty, Department of Industrial

Engineering / Student assistantship

01.07.2013-03.02.2015: Sakarya İz Reklam Ltd. Şti/Arifiye/Sakarya- Quality Engineer

20.04.2015-Continue: Istanbul Medipol University , Engineering Faculty, Department

of Industrial Engineering / Research Assistant

38

PUBLICATIONS

Kasap L. G., Vayvay Ö., Tuzkaya G., (2015). Afet Lojistiği ve Türkiye’deki

Uygulamaları: Literatür Araştırması. IV. Ulusal Lojistik Ve Tedarik Zinciri Yönetimi

Kongresi, Gümüşhane, 1-10 (Loder Journal, January 2016)