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UNIVERSITI PUTRA MALAYSIA
INTER-CELL AND INTRA-CELL FACILITY LAYOUT MODELS UNDER DIFFERENT DEMAND ENVIRONMENTS IN CELLULAR MANUFACTURING SYSTEMS
SHAHRAM ARIAFAR
FK 2012 122
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INTER-CELL AND INTRA-CELL FACILITY LAYOUT MODELS UNDER
DIFFERENT DEMAND ENVIRONMENTS IN CELLULAR
MANUFACTURING SYSTEMS
By
SHAHRAM ARIAFAR
Thesis Submitted to the School of Graduate Studies, Universiti Putra Malaysia,
in Fulfillment of the Requirements for the Degree of Doctor of Philosophy
April 2012
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DEDICATION
Dedicated to my family for their love, encouragement and support
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Abstract of thesis presented to the Senate of Universiti Putra Malaysia in
fulfillment of the requirement for the degree of Doctor of Philosophy
INTER-CELL AND INTRA-CELL FACILITY LAYOUT MODELS UNDER
DIFFERENT DEMAND ENVIRONMENTS IN CELLULAR
MANUFACTURING SYSTEMS
By
SHAHRAM ARIAFAR
April 2012
Chairman: Professor Datin Napsiah Ismail, PhD
Faculty : Engineering
Facility layout aims to arrange the facilities, including aisles, machines, instruments
and tools in the shop floor to provide an efficient layout. Efficiency of layout has
tremendous effects on the performance of the system. Cellular Manufacturing
System (CMS), as a promising manufacturing system has emerged to manufacture
mid-variety, mid-volume products. In a CMS parts based on their similarities are
classified into some groups named part families, and different machines are
dedicated to produce these parts. The aim of implementation of a CMS is to take
advantages of the similarities in the design and manufacturing of products.
In the design of cellular manufacturing systems, two important decisions should be
made; Cell Formation (CF), and Facility Layout (FL). Cell formation aims to group
part families and dedicate each part family to one or more machine cells. The
literature in the CF is extensive and abundant but layout models have not absorbed
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the attention of researchers as much as cell formation. Hence, in the first part of the
study, a facility layout model in a CMS is developed that considers unknown
locations for machine cells. The model minimizes both inter-cell and intra-cell
material handling cost. To solve the model, an algorithm based on Simulated
Annealing (SA) is developed in C/C++ namely SA1. Comparison of the results with
an adapted algorithm from the literature, in terms of the quality of solutions (material
handling cost) shows that the proposed algorithm produces better solutions with a
maximum of 0.08% error compared to 0.12% error in the benchmarked algorithm.
Moreover, the computation time (CPU Time) of the developed SA algorithm is
significantly less than the benchmarked algorithm. In the worst case, the proposed
algorithm finds good solutions about 50 times faster than the benchmarked.
Cellular manufacturing system is an important technique in the planning and control
of manufacturing systems. There are a lot of success stories about its implementation
but rapid changes in manufacturing systems may cause a CMS not to perform well in
every case. Therefore, it is quite important to investigate the effects of uncertainty of
demand of products on the layout of facilities in a cellular manufacturing system. For
this purpose, two mathematical models for layout in a CMS are proposed, which
considers the stochastic nature of demand. One of the models considers a Normal
distribution function for demand, and another one, a Uniform distribution function.
In order to validate the models several cases are generated and demonstrated by two
methods, Lingo 12.0 optimization software and an enumeration algorithm which is
developed in C/C++. The results show that uncertainty in the demand of products can
lead to changes in the arrangement of facilities.
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In a volatile manufacturing system, implementation of a CMS might not be
applicable. In such a situation, the use of a Hybrid Cellular Manufacturing (HCM)
seems to be more reasonable. Hence, in another part of this study, a model for layout
of facilities in a HCM is developed that considers demand of products varies in the
planning horizon. To solve the mathematical model, the SA1 is improved, and called
SA2. Comparison of the results shows that the SA2 produces better solution quality
in terms of the material handling cost with a maximum of 0.06% error compared to
0.08% error in the SA1. In addition, the computation time (CPU Time) of the SA2 is
nearly half of the SA1.
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Abstrak of tesis yang dikemukakan kepada Senat Universiti Putra Malaysia
sebagai memenuhi keperluan untuk ijazah Doktor Falsafah
MODEL SUSUN ATUR KEMUDAHAN INTRO SEL DAN ANTARA SEL
UNTUK PERSEKITARAN PERMINTAAN YANG BERBEZA DALAM
SISTEM PEMBUATAN SELULAR
Oleh
SHAHRAM ARIAFAR
April 2012
Pengerusi: Profesor Datin Napsiah Ismail, PhD
Fakulti : Kejuruteraan
Susun atur kemudahan bertujuan untuk menguruskan kemudahan, termasuk lorong,
mesin, instrumen dan alatan di lantai kilang untuk menyediakan susun atur yang
cekap. Kecekapan susun atur mempunyai kesan besar terhadap prestasi sistem.
Sistem Pembuatan Selular (CMS), sebagai sistem pembuatan yang baru telah muncul
untuk mengeluarkan produk pertengahan pelbagai pertengahan isipadu. Dalam CMS
bahagian kerja adalah berdasarkan persamaan dan diklasifikasikan kepada beberapa
kumpulan yang dinamakan kekeluargaan bahagian, dan mesin yang berlainan
berdedikasi untuk menghasilkan bahagian ini. Tujuan pelaksanaan CMS adalah
untuk mengambil kelebihan kepada persamaan dari segi reka bentuk dan pembuatan
produk.
Dalam reka bentuk sistem pembuatan selular, dua keputusan penting perlu dibuat;
Pembentukan Sel (CF), dan Kemudahan Susun Atur (FL). Pembentukan sel
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bertujuan untuk mengumpulkan kekeluargaan bahagian dan mendedikasikan setiap
keluarga bahagian kepada satu atau lebih sel mesin. Kajian ilmiah dalam CF adalah
luas dan banyak tetapi model susun atur yang tidak menarik perhatian penyelidik
sebanyak pembentukan sel. Maka, bahagian pertama dari kajian ini, model susun atur
kemudahan CMS dibangunkan yang menganggap lokasi sel-sel mesin tidak
diketahui. Model ini mengurangkan kos pengendalian bahan untuk kedua-dua antara
sel dan intra sel. Untuk model, algoritma berdasarkan penyepuhlindapan tersimulasi
(SA) dibangunkan dalam perisian C/C++ dinamakan SA1. Perbandingan keputusan-
keputusan dalam soal kualiti penyelesaian (kos pengendalian bahan) menunjukkan
yang algoritma dicadangkan menghasilkan penyelesaian yang lebih baik dengan ralat
maksimum 0.08% berbanding dengan 0.12% dalam algoritma tanda aras. Tambahan
pula, masa pengiraan (CPU Time) algoritma SA yang dicadangkan adalah kurang
sekali daripada algoritma tanda aras. Dalam kes paling teruk, algoritma dicadangkan
mendapati penyelesaian yang baik kira 50 kali terdahulu daripada tanda aras.
Sistem pembuatan selular adalah satu teknik yang penting dalam perancangan dan
kawalan sistem pembuatan. Terdapat banyak kisah kejayaan tentang pelaksanaan nya
tetapi perubahan yang pesat sistem pembuatan boleh menyebabkan CMS tidak
menunjukkan prestasi yang baik dalam semua kes. Oleh itu, ia cukup penting untuk
menyiasat kesan permintaan produk yang tidak menentu bagi susun atur kemudahan
dalam sistem pembuatan selular. Untuk tujuan ini, dua model matematik
dicadangkan dalam CMS, yang menganggap sifat stokastik permintaan. Satu
daripada model menganggap fungsi taburan Normal untuk permintaan, dan satu lagi
menganggap fungsi taburan sekata. Dalam usaha untuk mengesahkan model
beberapa kes dihasilkan dan ditunjukkan oleh dua kaedah, iaitu perisian
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pengoptimuman Lingo 12.0 dan algoritma penghitungan yang dibangunkan dalam
perisian C/C++. Keputusan menunjukkan bahawa sebarang ketidaktentuan dalam
permintaan produk boleh membawa kepada perubahan dalam susunan kemudahan.
Dalam sistem pembuatan yang tidak menentu, pelaksanaan CMS tidak mungkin
boleh diterima pakai. Dalam keadaan sedemikian, penggunaan Pembuatan selular
Hibrid seolah-olah menjadi lebih munasabah. Oleh itu, dalam bahagian lain kajian
ini, model bagi susun atur kemudahan HCM dibangunkan yang mempertimbangkan
permintaan pengeluaran berbeza di ufuk perancangan. Untuk menyelesaikan model
matematik, teknik SA1 ditambah baik, dan dinamakan teknik SA2. Perbandingan
keputusan menunjukkan yang algoritma SA2 menghasilkan kualiti penyelesaian
lebih baik dalam soal pengendalian bahan kos dengan maksimum 0.06% ralat
berbanding dengan 0.08% ralat daripada SA1. Sebagai tambahan, masa pengiraan
(CPU Time) algoritma SA2 ialah hampir separuh daripada SA1.
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ACKNOWLEDGEMENTS
By the name of Allah, the companionate, the merciful who gave me the power,
strength and patience to complete this study.
The accomplishment of this research is attributed to the firm support from my
supervisor, Professor Datin Dr. Napsiah Ismail. Her invaluable advice and help are
always there to bring me out of the darkness of failures and lead to the next bright
success.
Many thanks to my committee members, Dr. Tang Sai Hong and Dr. Mohd Khairol
Anuar Mohd Ariffin, their valuable comments and suggestions helped me to improve
the quality of this research.
I would like to express a full heart of thankfulness to my wife. Her great support and
patience have enabled me to complete this long journey of fulfilling my dream.
At last but not least, I would like to express my sincere thanks to my parents and
family; without their support, I would have never accomplished anything.
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I certify that a Thesis Examination Committee has met on 5 April 2012 to conduct
the final examination of Shahram Ariafar on his PhD thesis entitled “Inter-cell and
Intra-cell Facility Layout Models Under Different Demand Environments in Cellular
Manufacturing Systems” in accordance with the Universities and University College
Act 1971 and The Constitution of the Universiti Putra Malaysia [P.U. (A) 106] 15
March 1998. The Committee recommends that the student be awarded the Doctor of
Philosophy.
Members of the Examination Committee were as follows:
Shamsuddin Sulaiman, PhD
Professor
Faculty of Engineering
University Putra Malaysia
(Chairman)
Zulkiflle Leman, PhD
Associate Professor
Faculty of Engineering
University Putra Malaysia
(Internal Examiner)
Norzima Zulkifli, PhD
Senior Lecturer
Faculty of Engineering
University Putra Malaysia
(Internal Examiner)
Krishna Krishnan, PhD
Professor
Faculty of Engineering
Wichita State University
United States of America
(External Examiner)
SEOW HENG FONG, PhD
Professor and Deputy Dean
School of Graduate Studies
Universiti Putra Malaysia
Date: 21 May 2012
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This thesis was submitted to the Senate of Universiti Putra Malaysia and has been
accepted as fulfillment of the requirement for the degree of Doctor of Philosophy.
The members of the Supervisory Committee were as follows:
Napsiah Ismail, PhD
Professor
Faculty of Engineering
University Putra Malaysia
(Chairman)
Mohd Khairol Anuar Mohd Ariffin, PhD
Associate Professor
Faculty of Engineering
University Putra Malaysia
(Member)
Tang Sai Hong, PhD
Associate Professor
Faculty of Engineering
University Putra Malaysia
(Member)
BUJANG BIN KIM HUAT, PhD
Professor and Dean
School of Graduate Studies
Universiti Putra Malaysia
Date:
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DECLARATION
I declare that this thesis is my original work except for quotations and citations
which have been duly acknowledged. I also declare that it has not been previously,
and is not concurrently, submitted for any other degree at Universiti Putra Malaysia
or at any other institution.
AINI IDERIS, PhD
Profesor dan Dekan
Sekolah Pengajian Siswazah
Universiti Putra Malaysia
Tarikh:
SHAHRAM ARIAFAR
Date: 5 April 2012
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TABLE OF CONTENTS
Page
DEDICATION ii
ABSTRACT iii
ABSTRAK iv
ACKNOWLEDGEMENTS ix
APPROVAL x
DECLARATION xii
LIST OF ABBREVIATIONS xvi
CHAPTER
1. INTRODUCTION 1 1.1. Background 1
1.2. Problem Statement 3
1.3. Objectives 5
1.4. Contributions of the Study 6
1.5. Scope of the Study 6
1.6. Outline of Thesis 7
2. LITERATURE REVIEW 9 2.1. Introduction 9
2.2. Facility Layout in Cellular Manufacturing Systems 13
2.2.1. Classification Based on the Involved Decisions 15
2.2.1.1 Facility Layout and Cell Formation 15
2.2.1.2 Facility Layout and Flow Path 18
2.2.1.3 Group Scheduling 19
2.2.1.4 Group Layout 19
2.2.2. Objective Functions 22
2.2.3. Issues of Demand Uncertainties on the Layout 23
2.2.3.1 Dynamic Facility Layout Problems 24
2.2.3.2 Application of Fuzzy Theory 25
2.2.3.3 Stochastic Facility Layout Problems 25
2.2.4. Shop Layout 27
2.3. Mathematical Models 30
2.3.1. Facility Layout Models in a CMS 30
2.3.2. Uncertainty of Demand in Layout Models in a CMS 32
2.4. Overall Shortcomings of the Existing Models 36
2.5. Solution Approaches 38
2.5.1. Exact Methods 38
2.5.1.1 Enumerative Approach 38
2.5.1.2 Relaxation and Decomposition Approach 39
2.5.1.3 Cutting Plane Algorithms 39
2.5.2. Simulated Annealing 40
2.5.2.1 Mechanism of Generating Solutions 43
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2.5.2.2 Cooling Schedule 44
2.6. Summary 48
3. METHODOLOGY 49 3.1. Introduction 49
3.2. Models Formulation 49
3.2.1. Assumptions 52
3.2.2. Proposed Mathematical Models 53
3.2.2.1 Deterministic Facility Layout Model in a CMS 54
3.2.2.2 Stochastic Facility Layout Models in a CMS 56
3.2.2.3 A Facility Layout Model in a HCM under Variable Demand 61
3.3. Solution Approaches 64
3.3.1. Use of the Lingo Optimization Software 64
3.3.2. Enumeration Approach 64
3.3.3. Simulated Annealing 65
3.3.3.1 Implementation of Simulated Annealing 66
3.3.3.2 Proposed SA Algorithms 69
3.3.3.3 Difference between SA1 and SA2 74
3.3.3.4 Benchmarked Algorithm 74
3.4. Summary 75
4. MODELS VERIFICATION 76 4.1. Introduction 76
4.2. Validation of the Model 76
4.2.1. Case 1 77
4.2.2. Case 2 79
4.3. Summary 81
5. AN IMPROVED ALGORITHM FOR LAYOUT DESIGN IN CELLULAR
MANUFACTURING SYSTEMS 82
Article
Copyright/ Permission Letter
6. A STOCHASTIC FACILITY LAYOUT MODEL IN CELLULAR
MANUFACTURING SYSTEMS 110
Article
Copyright/ Permission Letter
7. THE RECONFIGURATION ISSUE OF STOCHASTIC FACILITY
LAYOUT DESIGN IN CELLULAR MANUFACTURING SYSTEMS 126
Article
Copyright/ Permission Letter
8. A LAYOUT DESIGN MODEL IN HYBRID CELLULAR
MANUFACTURING SYSTEMS 147
Article
Copyright/ Permission Letter
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9. CONCLUSIONS AND RECOMMENDATIONS OF FUTURE WORKS
172 9.1. Introduction 172
9.2. Summary 172
9.2.1. Achievements of the First Objective 173
9.2.2. Achievements of the Second Objective 175
9.2.3. Achievements of the Third Objective 176
9.3. Recommendation of Future Works 178
REFERENCES 180
APPENDICIES 194
BIODATA OF STUDENT 199
LIST OF PUBLICATIONS 200
