ant colony optimization for the single model u-type assembly line balancing problem

185 Pages·2004·1.1 MB·English

ANT COLONY OPTIMIZATION FOR
THE SINGLE MODEL U-TYPE
ASSEMBLY LINE BALANCING PROBLEM

A THESIS
SUBMITTED TO THE DEPARTMENT OF INDUSTRIAL ENGINEERING
AND THE INSTITUTE OF ENGINEERING AND SCIENCE
OF BILKENT UNIVERSITY
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
MASTER OF SCIENCE

By
Arda Alp
January, 2004
I certify that I have read this thesis and that in my opinion it is fully adequate,
in scope and in quality, as a thesis for the degree of Master of Science.

Prof. Dr. İhsan Sabuncuoğlu (Supervisor)

I certify that I have read this thesis and that in my opinion it is fully adequate,
in scope and in quality, as a thesis for the degree of Master of Science.

Prof. Dr. Erdal Erel

I certify that I have read this thesis and that in my opinion it is fully adequate,
in scope and in quality, as a thesis for the degree of Master of Science.

Asst. Prof. Dr. Mehmet Taner

Approved for the Institute of Engineering and Sciences:

Prof. Dr. Mehmet Baray
Director of the Institute of Engineering and Science
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ABSTRACT

ANT COLONY OPTIMIZATION FOR
THE SINGLE MODEL U-TYPE
ASSEMBLY LINE BALANCING PROBLEM

Arda Alp
M.S. in Industrial Engineering
Supervisor: Prof. Dr. İhsan Sabuncuoğlu
January, 2004

The assembly line is a production line in which units move continuously through a
sequence of stations. The assembly line balancing problem is the allocation of tasks
to an ordered sequence of stations subject to the precedence constraints with the
objective of minimizing the number of stations. In a U-line the line is configured into
a U-shape topology.
In this research, a new heuristic, Ant Colony Optimization (ACO) meta-heuristic,
and its variants are proposed for the single model U-type assembly line balancing
problem (UALBP). We develop a number of algorithms that can be grouped as:
(i) direct methods, (ii) modified methods and (iii) methods in which ACO approach is
augmented with some metaheuristic.
We also construct an extensive experimental study and compare the performance of
the proposed algorithms against the procedures reported in the literature.

Keywords: U-type assembly line balancing problem, Ant Colony Optimization meta-
heuristic.

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ÖZET

TEK MODELLİ U-TİPİ
MONTAJ HATTI DENGELENMESİ PROBLEMİ İÇİN
KARINCA KOLONİSİ OPTİMİZASYONU

Arda Alp
Endüstri Mühendisliği, Yüksek Lisans
Tez Yöneticisi: Prof. Dr. İhsan Sabuncuoğlu
Ocak, 2004

Montaj hattı ürünlerin sıralı olarak istasyonlardan geçtiği bir üretim hattıdır. Tek
modelli montaj hattı dengelenmesi problemi istasyon sayısının en küçüklenmesi
amacına yönelik olarak işlerin öncüllük kısıtı dikkate alınarak sıralı istasyonlara
atanmasıdır. U-tipi hatta ise üretim hattı U şeklinde düzenlenmiştir.
Bu çalışmada yeni bir sezgisel olan Karınca Kolonisi Optimizasyonu (KKO) ve
çeşitleri U-tipi montaj hattı dengelenmesi problemi için önerilmiştir. Geliştirilen
algoritmalar üç grup altında incelenebilir: (i) direkt metodlar, (ii) modifiye edilmiş
metodlar ve (iii) KKO yaklaşımının diğer bazı sezgisellerle beraber kullanıldığı
metodlar.
Ayrıca kapsamlı bir deneysel çalışma yapılmış ve önerilen algoritmaların
performansı literatürdeki diğer metodlarla karşılaştırılmıştır.

Anahtar sözcükler: U-tipi montaj hattı dengelenmesi problemi, Karınca Kolonisi
Optimizasyonu meta-sezgiseli.

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To my family,

v
Acknowledgement

I would like to express my deepest gratitude to my supervisor Prof. Dr. İhsan
Sabuncuoğlu for his instructive comments in the supervision of the thesis and also for
all the encouragement and trust during my graduate study.
I am also indebted to Prof. Dr. Erdal Erel for his invaluable guidance,
recommendations and everlasting interest for this research and for my future work.
I would like to express my special thanks and gratitude to Asst. Prof. Dr. Mehmet
Taner for showing keen interest to the subject matter, for his remarks,
recommendations and accepting to read and review the thesis.
I am grateful to Asst. Prof. Bahar Yetiş Kara for her understanding and her
recommedations and also to Asst. Prof. Oya Karaşan for her suggestions and her
guidance.
I would like to express my deepest thanks to Banu Yüksel for all her encouragement
and academic support. I would like to extend my sincere thanks to Savaş Çevik, Nur
Beğen, Emrah Zarifoğlu and Pınar Tan. Their endless morale support and friendship
during all my desperate times, makes me to face with all the troubles.
Finally, I would like to express my gratitude to my family for their love,
understanding, suggestions and their endless support. I owe so much to my family.

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Contents

Abstract..............................................................................................................iii
Özet.....................................................................................................................iv
Acknowledgement.............................................................................................vi
Contents............................................................................................................vii
List of Figures....................................................................................................xi
List of Tables...................................................................................................xiv
1 Introduction...................................................................................................1
2 Literature Survey........................................................................................10
2.1 Ant Colony Optimization Meta-Heuristic...........................................10
2.2 U-Type Line Balancing........................................................................21
3 Ant Algorithms and Applications.............................................................28
3.1 Introduction...........................................................................................29
3.2 Biological Fundamentals......................................................................30
3.3 The Ant Colony Optimization Approach.............................................35
3.3.1 Similarities and Difference with Real Ants..............................36
3.4 The ACO Meta-heuristic......................................................................38
3.5 Some Applications of ACO Algorithms..............................................42

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4 Proposed Approach: Ant Colony Optimization.....................................43
4.1 Overview of the Proposed Approaches................................................43
4.1.1 Motivation.................................................................................43
4.1.2 Fundamentals............................................................................44
4.1.2.1 The Graph Representation of the Problem................45
4.1.2.2 The Autocatalytic Process..........................................46
4.1.2.3 The Greedy Force.......................................................46
4.1.2.4 The Constraint Satisfaction........................................48
4.1.3 Generation of a Solution...........................................................49
4.2 Proposed Methods................................................................................56
4.2.1 Ant System (AS)........................................................................56
4.2.2 Ant System with Elitist Strategy (AS ).................................61
elite
4.2.3 Ant System with Ranking (AS )............................................62
rank
4.2.4 Ant Colony System (ACS)........................................................63
4.2.5 Modified Ant Colony System (ACS) with Random Search ...68
4.2.6 A New Ant Colony Optimization (ACO) Method ..................76
Version 1....................................................................................77
Version 2....................................................................................83
4.2.7 Ant Colony System Augmented with Simulated Annealing
(ACS with SA)...........................................................................86

4.2.8 Ant Colony System Augmented with Beam Search
(ACS with BS)...........................................................................86

viii
5 Experimental Setting..................................................................................88
5.1 Experimental Setting for AS................................................................94
5.1.1 Number of Ants.........................................................................94
5.1.2 Parameters Setting.....................................................................96
5.2 Experimental Setting for AS ............................................................98
elite
5.2.1 Number of Ants.........................................................................98
5.2.2 Parameters Setting...................................................................100
5.3 Experimental Setting for ACS............................................................102
5.3.1 Number of Ants.......................................................................102
5.3.2 Parameters Setting...................................................................104
5.4 Experimental Setting for Modified ACS with Random Search........106
5.5 Experimental Setting for New ACO Approach, Version 1...............106
5.5.1 Number of Ants.......................................................................106
5.5.2 Parameters Setting...................................................................108
5.6 Experimental Setting for New ACO Approach, Version 2...............113
5.6.1 Number of Ants.......................................................................113
5.6.2 Parameters Setting...................................................................115
6 Computational Results.............................................................................116
6.1 Computational Results for AS............................................................118
6.2 Computational Results for AS .......................................................123
elite
6.3 Computational Results for ACS.........................................................128
6.4 Computational Results for Modified ACS with Random Search.....133

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6.5 Computational Results for New ACO Approach, Version 1............134
6.6 Computational Results for New ACO Approach, Version 2............141
7 Conclusion.................................................................................................147
Bibliography...................................................................................................151
Appendix.........................................................................................................159
A1 Appendix A.........................................................................................159
A2 Appendix B.........................................................................................163
A3 Appendix C.........................................................................................165
A4 Appendix D.........................................................................................168

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