job shop conversion of manufacturing system to …

JOB SHOP CONVERSION OF MANUFACTURING

SYSTEM TO CELLULAR MANUFACTURING

BY

SANAA ALI HAMZA

A thesis submitted in fulfilment of the requirement for the

degree of Doctor of Philosophy in Engineering

Kulliyyah of Engineering

International Islamic University Malaysia

AUGUST 2014

ii

ABSTRACT

Recently, the Cellular Manufacturing (CM) which is based on the Group Technology

(GT) principles is seen to be the alternative system to handle the challenges for the

global manufacturing systems. The main objective of the current research is to

develop a tool for integrating the Feasibility Assessment (FA) with the Cell Formation

(CF). This tool aims to prevent the failure after applying the CM. To get the benefits

of this tool, firstly a Similarity Coefficient (SC) based method was proposed to

complete the work in the FA stage, and then another SC based method was developed

to complete the work in the CF stage, afterward; the two methods integrated together

by using two assumptions and the same SC measure. The developed tool performs

better than the previous well-known SC based methods when tested, using nine

performance measures selected from literature. Also, it can apply smoothly in the job

shop based factories.The significant of the developed tool is that, the factories can use

this tool as a decision support tool to test the existing data before applying the Cellular

Manufacturing System (CMS). In addition the factories can control on the conversion

process from evaluation the data to application of the CMS. The main question of the

problem statement is how to integrate the FA into CF. Next how to solve all the

questions related to complete the integration process. The main objectives to solve the

problem statement are: to propose a method for FA, to develop a method for CF and

to integrate between these two methods. The methodology that followed to achieve

the objectives is as follows: firstly a method based on SC measures was applied to

identify (the decision of applying the CM, the predicted number of machine cells and

the quality of the solution) in the FA stage. Secondly, another method also based on

using SC was developed to create machine cells and part families in the CF. Then

these two methods were integrated together, using two assumptions and the same SC

measure in both stages the FA and CF. For validating work, the developed tool

applied in three factories located in Malaysia and Iraq. For more validation the results

of the developed tool compare with the results of some well-known methods known as

Agglomerative Hierarchical Clustering (AHC). The results prove that the developed

tool perform better than the previous AHC methods and leads to increase the grouping

efficacy, reduce the intercellular moves and saved the material handling cost. Through

the current research new performance measure called integrated index was developed

to integrate the quality of the solution in the FA and CF. Moreover, for the same

purpose, new mathematical model solved, using Genetic Algorithm (GA) was

developed. The contribution to knowledge for this research is the developing of tool,

mathematical model and index for integrating the FA and CF. For the future work, it

is suggested to use part attribute matrix, other SC measures based on real life

production features and other design stages such as cell layout, cell schedule to

integrate with the FA.

iv

APPROVAL PAGE

The thesis of Sanaa Ali Hamza has been approved by the following:

_____________________________

Erry Yulian Triblas Adesta

Supervisor

_______________________________

Jamal I. Daoud

Co-Supervisor

_______________________________

Mohd Radzi Che Daud

Co-Supervisor

_______________________________

Mohammad Iqbal

Co-Supervisor

_______________________________

Md. Yusof Ismail

Internal Examiner

_______________________________

S.G. Ponambalam

External Examiner

_______________________________

Indra Putra Almanar

External Examiner

_______________________________

Radwan Jamal Yousef EL Atrash

Chairperson

v

DECLARATION

I hereby declare that this dissertation is the result of my own investigations, except

where otherwise stated. I also declare that it has not been previously or concurrently

submitted as a whole for any other degrees at IIUM or other institutions.

Sanaa Ali Hamza

Signature Date

vi

INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA

DECLARATION OF COPYRIGHT AND AFFIRMATION

OF FAIR USE OF UNPUBLISHED RESEARCH

Copyright © 2014 by Sanaa Ali Hamza. All rights reserved.

JOB SHOP CONVERSION OF MANUFACTURING SYSTEM TO

CELLULAR MANUFACTURING

No part of this unpublished research may be reproduced, stored in a retrieval system,

or transmitted, in any form or by any means, electronic, mechanical, photocopying,

recording or otherwise without prior written permission of the copyright holder

except as provided below.

1. Any material contained in or derived from this unpublished research

may only be used by others in their writing with due

acknowledgement.

2. The IIUM or its library will have the right to make and transmit copies

(print or electronic) for institutional and academic purpose.

3. The IIUM library will have the right to make, store in a retrieval

system and supply copies of this unpublished research if requested by

other universities and research libraries.

Affirmed by Sanaa Ali Hamza

Signature Date

vii

I dedicate this work to our beloved Prophet Muhammad (May God’s

prayers and peace be upon him) and to his favorite daughter and my

greatest example of Muslim woman Fatima (peace be upon her)

viii

ACKNOWLEDGEMENTS

I am thankful to Almighty Allah who gave me the strength and wisdom to complete

this research work. It has been a long journey to finally complete one of my dreams.

Many people have been involved in this journey. I must admit that without their

support and guidance, it would have been much more difficult to accomplish this

success. At this time, I would like to thank each of them for their encouragement and

support.

To begin, I would like to express my thanks and appreciation to my supervisor,

Prof. Dr. Erry Yulian Triblas Adesta, thanks for your availability, expertise and

advice, motivation, and encouragement to move forward.

I would also like to express my special thanks and appreciation to the

management of Royal Selangor factory-Malaysia. Also, I am extremely thankful to

the management and staff of the State Company for Mechanical Industries –Iraq.

My most valuable support by far has come from my lovely Mum and Dad, and

all my brothers. Thank you for a lifetime of support, love, and care. Finally, many

thanks go to my colleagues and friends at IIUM. It is in great part due to them that I

had such an enjoyable and memorable time in Malaysia.

ix

TABLE OF CONTENTS

Abstract ................................................................................................................... ii

Abstract in Arabic ................................................................................................... iii

Approval Page ......................................................................................................... iv

Declaration Page ..................................................................................................... v

Copyright Page ........................................................................................................ vi

Dedication ............................................................................................................... vii

Acknowledgements ................................................................................................. viii

List of Tables .......................................................................................................... xv

List of Figures ......................................................................................................... xx

List of Abbreviations .............................................................................................. xxiv

List of Symbols ....................................................................................................... xxvi

CHAPTER ONE: INTRODUCTION ................................................................. 1 1.1 Introduction ............................................................................................ 1

1.1.1. Overview of Group Technology ................................................. 2 1.1.2. Group Technology Definition ..................................................... 3

1.1.3. Process (Job Shop) Layout .......................................................... 3 1.1.4. Cellular (Group) Layout .............................................................. 4

1.1.5. Methods of Identifying Families and Cells ................................. 5 1.1.6. Types of Matrices in Cellular Manufacturing System ................ 8 1.1.7. Benefits of Cellular Manufacturing ............................................ 10

1.1.8. Cell Formation Methods ............................................................. 11 1.1.9. Design of Cellular Manufacturing System .................................. 12

1.1.10. Design of Cell Formation .......................................................... 13 1.1.11. Types of Cellular Manufacturing .............................................. 14

1.1.11.1. Static Cellular Manufacturing ......................................... 14 1.1.11.2. Dynamic Cellular Manufacturing ................................... 14

1.1.12. Evaluation of CM Solution ....................................................... 15

1.2. Problem Statement and it’s Significance .............................................. 16 1.3. Research Objectives .............................................................................. 17

1.4. Philosophy of the Work ........................................................................ 18 1.5. Outline of Research Methodology ........................................................ 19 1.6. Scope of the Research ........................................................................... 21

1.7. Thesis Contributions ............................................................................. 22 1.8. Thesis Organization .............................................................................. 22

CHAPTER TWO: LITERATURE REVIEW: CELLULAR

MANUFACTURING DESIGN TECHNIQUES ................................................. 24 2.1. Introduction ........................................................................................... 24 2.2. Studies in Feasibility Assessment Phase ............................................... 25

2.3. Classification of Cellular Manufacturing Techniques .......................... 26 2.4. Similarity Coefficient (SC) Based Approaches .................................... 27

2.4.1. Taxonomy of Similarity Coefficients Measures ......................... 29 2.4.2. Similarity Coefficients Measures ................................................ 30

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2.4.3. Comparative Studies of Similarity Coefficient Measures

(SCMs) .................................................................................................. 31 2.4.4. Flexibility of SC Based Methods ................................................ 33

2.4.5.Incorporating the Production Features in Similarity Cofficient

Measures ............................................................................................... 34 2.4.6. Agglomerative Hierarchical Clustering (AHC) Methods ........... 35

2.4.6.1. Single Linkage Clustering Algorithm (SLCA) ................. 36 2.4.6.2. Complete Linkage Clustering Algorithm (CLCA) ........... 39

2.4.6.3. Average Linkage Clustering Algorithm (ALCA) ............. 40 2.4.6.4. Ward’s Method ................................................................. 41 2.4.6.5. Weighted Pair Group Average (WPGA) Method ............. 41 2.4.6.6. Un-weighted Pair Group Average (UWPGA) Method ..... 42

2.4.7. Comparison of Agglomerative Hierarchical Clustering

Algorithms ............................................................................................ 42

2.5. Integration of the Basic Decisions in CM ............................................. 43

2.5.1. Cell Formation Studies ................................................................ 44 2.5.2. Studies on Integrating Cell Formation and Cellular Layout ....... 47 2.5.3. Studies on Integrating the Cell Formation, Cellular Layout

and Cell Scheduling .............................................................................. 51

2.5.4. Genetic Algorithm for Integrating the Basic Decisions of CM .. 53 2.5.5. Limitations of the Integration Processes ..................................... 54

2.6. Evaluation of Cellular Manufacturing Solutions .................................. 55 2.6.1. Grouping Efficiency (GE) ........................................................... 56 2.6.2. Grouping Efficacy (GC) .............................................................. 57

2.6.3. Number of the Exceptional Elements (EE) ................................. 59 2.6.4. Percentage of the Exceptional Elements (PE) ............................. 59

2.6.5. Machine Utilization (MU) .......................................................... 60

2.6.6. Machine Utilization Index (Grouping Measure, GM) ................ 61

2.6.7. Linear Performance Measure ...................................................... 61 2.6.8. Grouping Capability Index (GCI) ............................................... 61 2.6.9. Ratio of non-zero Elements in Cells (REC) ................................ 62

2.6.10. Ratio of the Exceptions (RE) .................................................... 62

2.6.11. Sum of Similarities and the Perfection Percentage ................... 63 2.6.12. Modified Grouping Efficiency (MGE) ..................................... 63 2.6.13. Number of the Intercellular Moves ........................................... 64 2.6.14. Comparsion the Number of Cells between the FA and CF ....... 65 2.6.15. Single Machine Cells ................................................................ 65

2.7. Summary ............................................................................................... 68

CHAPTER THREE: RESEARCH METHODOLOGY ................................... 69 3.1. Introduction ........................................................................................... 69 3.2. Motivation of Using Various Similarity Coefficient Measures ............ 70

3.2.1. Methodology for comparison with Basher and Karaa’s study .... 70 3.2.2. Heuristic Procedures of the Comparison Methodology .............. 71

3.3. Methods for Identifying the Number of machine cells in the FA ......... 72 3.3.1. Methodology for selecting method to identify the number of

machine cells ......................................................................................... 73 3.3.2. Heuristic Procedures for selecting method to identify the

number of machine cells ....................................................................... 74

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3.3.2.1. First Method (the mmax Based Method) ............................ 74

3.3.2.2. Second Method (the SC Based Method) ........................... 75 3.4. Similarity Coefficient based Approach for the Feasibility

Assessment ................................................................................................... 76 3.4.1. Methodology for the Feasibility Assessment .............................. 76 3.4.2. Heuristic Procedures for the Feasibility Assessment .................. 78 3.4.3. The Similarity Coefficient Measures Based on the Production

Features for the FA................................................................................ 79

3.5. The Evaluation of the scms in the FA ................................................... 80 3.5.1. Methodology for Evaluating the SCMs by using the Predicted

Number of Machine Cells ..................................................................... 80 3.5.2. Methodology for Evaluating the SCMs by using the

Clustering Tendency Index ................................................................... 80

3.6. Similarity Coefficient based Approach for the Cell Formation ............ 82

3.6.1. Methodology for the Cell Formation .......................................... 82

3.6.2. Heuristic Procedures for the Cell Formation .............................. 83 3.6.3. Similarity Coefficient Measure Based on the Production

Features for the Cell Formation ............................................................ 84 3.7. Integrating the FA and CF ..................................................................... 85

3.7.1. Methodology for Integrating the FA and CF .............................. 86 3.7.2. Heuristic Procedures for Integrating the FA and CF .................. 87

3.7.2.1. First Method (Based on the same SCM) ........................... 87 3.7.2.2. Second Method (Based on the different SCMs) ............... 88 3.7.2.3. Third Method (Based on the SCM and ROC)................... 88

3.8. Agglomerative Hierarchical Clustering Approaches (AHC) for the

Cell Formation ............................................................................................. 89

3.8.1. Methodology for Applying the AHC Approaches ...................... 90

3.8.2. Heuristic Procedures for Applying the AHC in the Cell

Formation .............................................................................................. 91 3.9. Evaluation Procedures ........................................................................... 91

3.9.1. Methodology of Applying the Evaluation Procedures ................ 92

3.9.2. Heuristic Procedures for the Evaluation ..................................... 93

3.10. Summary ............................................................................................. 95

CHAPTER FOUR: THE IMPLEMENTATION OF THE FEASIBILITY

ASSESSMENT (FA) AND CELL FORMATION (CF) ..................................... 97 4.1. Introduction ........................................................................................... 97

4.2. Motivation of Using Various Scms ....................................................... 99 4.3. Sc Based Approach for the Feasibilite Assessment .............................. 99

4.3.1. Identify the Predicted Number of Machine Cells ....................... 99

4.3.1.1. First Method (the mmax Based Method) ............................ 100 4.3.1.2. Second Method (the SC Based Method) ........................... 102

4.3.2. The Evaluation of the SCMs in the FA ....................................... 103 4.3.2.1. The Predicted Number of Machine Cells .......................... 103

4.3.2.2. The Clustering Tendency Index ........................................ 107 4.4. SC Based Approach for the Cell Formation ......................................... 118

4.4.1. The Distance Matrices Calculations............................................ 119 4.4.2. Creating the Machine Cells ......................................................... 124 4.4.3. Creating the Machine Cells and Part Families ............................ 130

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4.5. Incorporating of the Production Features in the Proposed Method

for the FA And CF ....................................................................................... 132 4.6. Integrating the Feasibility Assessment and Cell Formation ................. 140

4.6.1. Methodology for Integrating the FA and CF .............................. 141 4.7. New Mathematical Model for Integrating the FA And CF ................... 148 4.8. New Performance Measure for Identifying the Goodness of the Cm

Solutions ....................................................................................................... 153 4.9. Validating the Proposed Method by Applying the Agglomerative

Hierarchical Clustering Methods ................................................................. 154 4.9.1. Cell Formation by Using the AHC.............................................. 154 4.9.2. The Dendrogram ......................................................................... 155 4.9.3. Creating the Machine Cells and Part Families ............................ 158

4.10. Summary ............................................................................................. 160

CHAPTER FIVE: CASE STUDIES IN MALAYSIA AND IRAQ .................. 161 5.1 Introduction ............................................................................................ 161 5.2 The State Company for Mechanical Industries (SCMI) in Iraq ............. 162

5.2.1 Types of (SCMI) Products ........................................................... 163 5.2.2 Factories in (SCMI)...................................................................... 163

5.2.3 The Production Volume of SCMI ................................................ 164 5.2.4 The Sales Volume of Products in SCMI ...................................... 164

5.3 Case One: Production Requirements Factory-Iraq ................................ 165 5.3.1. Case Study and Implementation.................................................. 165 5.3.2 Machines Information Analysis ................................................... 167

5.3.3 Products (Parts) Information Analysis ......................................... 168 5.3.4 Machine-Part Information Analysis ............................................. 168

5.3.5 Application of the Conversion Methodology ............................... 169

5.3.6 Final Manufacturing Cells and Part Families .............................. 171

5.4 Case Two: Agricultural Implements Factory-Iraq ................................. 174 5.4.1 Machine Information Analysis ..................................................... 179 5.4.2 Products (Parts) Information Analysis ......................................... 181

5.4.3 Machine-Part Information Analysis ............................................. 185

5.4.4 Conversion Procedures ................................................................... 190 5.4.5 Final Manufacturing Cells and Part Families ................................... 190 5.4.6 Final Machine Cells Layout ......................................................... 193

5.5 Case Three: Royal Selangor Wood Factory ........................................... 197 5.5.1 Machine Information Analysis ..................................................... 201

5.5.2 Products (Parts) Information Analysis ......................................... 201 5.5.3 Machine-Part Information Analysis ............................................. 202 5.5.4 Conversion Procedures ................................................................ 204

5.5.5 Final Manufacturing Cells and Part Families .............................. 205 5.5.6 Final Machine Cells Layout ......................................................... 206

5.6 Summary ................................................................................................ 208

CHAPTER SIX: RESULTS AND DISCUSSION .............................................. 209 6.1. Introduction ........................................................................................... 209 6.2. The Details of Selected Datasets ........................................................... 209 6.3. Analyzing the Results of the SC Based Method for FA ....................... 212

6.3.1. Analyzing the Results of Utilizing Nineteen SCMs in the FA ... 212

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6.3.2. Analysing the Results of Methods Using for Identifying the

Number of Machine Cells in the FA ..................................................... 217 6.4. Evaluating the Performance of the SCMS in the FA ............................ 219

6.4.1. Analyzing the Results of the Predicted Number of Machine

Cells ..................................................................................................... 219 6.4.2. Analyzing the Results of the Clustering Tendency Index ........... 221

6.5. Analysing the Results of the SC based Method for the CF ................... 227 6.5.1. Analyzing the Performance of the SCMs in the proposed CF

(Through the research assumptions) ..................................................... 227 6.5.2. Analyzing the Performance of the Proposed Method in CF ....... 231

6.6. Analysing the Results (Performance) of the Combined SC based

Method for Integrating the FA and CF ........................................................ 233 6.7. Validation of the Proposed SC based Approach for Integrating the

FA and CF .................................................................................................... 236

6.7.1. Analyzing the Results of the (GC) .............................................. 239

6.7.2. Analyzing the Results of the (PE) ............................................... 240 6.7.3. Analyzing the Results of the (MU) ............................................. 241 6.7.4. Analyzing the Results of the GCI ............................................... 243 6.7.5. Analyzing the Results of the REC .............................................. 244

6.7.6. Analyzing the Results of the Linear Performance Measure........ 245 6.7.7. Analyzing the Results of the MUI .............................................. 246

6.7.8. Analyzing the Results of the IC Moves ...................................... 247 6.7.9. Analyzing the Results of the New Performance Measure

(Integrated Index) (II) ........................................................................... 248

6.7.10. Analyzing the Results of the Number of Cells in the FA and

CF ..................................................................................................... 250

6.7.11. Analyzing the Results of the Single Machine Cells .................. 251

6.8. Analysing the Results of Incorporating the Production Features in

the Proposed Method .................................................................................... 256 6.8.1. Incorporating the Production Features in the FA ........................ 256 6.8.2. Incorporating the Production Features in the CF ........................ 258

6.9. Analyzing the Results of Implemnting the New Mathematical

Model for the Theoretical Datasets .............................................................. 261 6.10. Analyzing the Results of the Real Life Datasets ................................. 261

6.10.1.Analyzing the Results of Implementing the New

Performance Measure............................................................................ 262 6.10.2.Analyzing the Results of Implementing the New

Mathematical Model ............................................................................. 263 6.11. Summary ............................................................................................. 264

CHAPTER SEVEN: CONCLUSIONS AND RECOMMENDATIONS ......... 265 7.1 Conclusion .............................................................................................. 265 7.2 Contribution to the Knowledge .............................................................. 268 7.3 Limitation of the Research ..................................................................... 269

7.4. Recommendations and Future Researches ............................................ 269

REFERENCES ....................................................................................................... 271

xiv

APPENDIX A:LIST OF PUBLICATION ............................................................... 288

APPENDIX B: MATLAB-PROGRAMMING ....................................................... 289 PRODUCTION FEATURES ................................................................................... 289

APPENDIX C: MATLAB-PROGRAMMING ....................................................... 293 PERFORMANCE MEASURES .............................................................................. 293 APPENDIX D: C++AND JAVA ............................................................................. 314 APPENDIX E: GENETIC ALGORITHM CODE .................................................. 321

xv

LIST OF TABLES

Table No. Page No.

1.1 Comparison among methods of identifying PFs & MCs (Burbidge,

1971) 7

1.2 Machine-part matrix, cells and families (Basher and Karaa, 2008) 9

1.3 Part-attribute matrix (Ghosh et al., 2011a) 10

2.1 Classification of the cell formation methods 26

2.2 Using various cell formation methods in the FA and CF 27

2.3 The twenty SCMs (general purpose and problem oriented) 31

2.4 Review of some integrating studies in the CM 55

2.5 Some literature review of the previous studies in the FA and CF 66

3.1 The performance measures that were used in the current research 92

3.2 Some programmes designed to complete the work in the current

research 93

4.1 The datasets utilized to identify the number of machine cells 101

4.2 The number of machine cells, using the m max based method 101

4.3 The number of machine cells, using the SC based method 102

4.4 The references of the datasets 104

4.5 The number of machine cells, using twenty SCMs for datasets with

various sizes a. Datasets ranged from (4*4) to (6*8) 105

4.6 Machine/Part Matrix for dataset (5*6) 107

4.7 Similarity coefficient matrices for thirty datasets, using Rogers and

Tanimoto measure 109

4.8 The Eigenvalues, number of cells and CI for dataset (5*6) 114

4.9 The clustering tendency index (CI) values for thirty datasets with

various sizes 115

4.10 The average values of the CI for each dataset, using twenty SCMs 117

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4.11 Distance matrices for dataset (5*8), using twenty SCMs 119




4.15 Steps of the proposed cell formation method for dataset (5*8) 125




4.19 Cells and families for dataset (5*8), using all the twenty SCMs 130

4.20 Cells and families for dataset (6*8), using seventeen SCMs 130

4.21 Cells and families for dataset (6*8), using (Kulczynski) measure 130

4.22 Cells and families for dataset (6*8), using (Sokal and Sneath 4)

measure 131

4.23 Cells and families for dataset (6*8), using (Relative matching)

measure 131

4.24 Cells and families for dataset (9*11), using all the twenty SCMs 131

4.25 Cells and families for dataset (10*10), using eighteen SCMs 131

4.26 Cells and families for dataset (10*10), using (Kulczynski) measure 132

4.27 Cells and families for dataset (10*10), using (Relative matching)

measure 132

4.28 The references of data sets for applying the production features 134

4.29 Sequence of operations for dataset (8*20) by using Jaccard measure 135

4.30 Sequence of operations for dataset (8*20) by using SCM based on

the production volume 135

4.31 Sequence of operations for dataset (8*20) by using SCM based on

the batch size 136

4.32 The Eigenvalues with and without using the production features in

the FA phase 137

4.33 The machine cells and machines inside cells with and without using

the production features in the CF 139

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4.34 The proposed methodology of the present study for integrating the

FA and CF 141

4.35 The method based on the same SCM 142

4.36 The method based on the different SCMs 144

4.37 The method based on the SCM and ROC 146

4.38 Machine cells and machines inside each cell by using six AHC

methods 155

4.39 Cells and families for dataset (5*8), using all the six AHC 159

4.40 Cells and families for dataset (6*8), using SLCA 159

4.41 Cells and families for dataset (6*8), using (Flexible, Weighted,

Unweighted, CLCA and Award) 159

4.42 Cells and families for dataset (10*10), using all the six AHC

methods 159

5.1 The production volume of each product in SCMI until 2003 164

5.2 The sales volume of products in SCMI until 2003 164

5.3 Operation plan and code for ten machines (machines information) in

PRF 167

5.4 Process plan for nine products (products information) in PRF 168

5.5 Machine-product information of PRF 169

5.6 The conversion procedures of PRF to cellular manufacturing 170

5.7 The final manufacturing cells and part families in PRF 172

5.8 The methodology steps in AIF 179

5.9 The machine information for all the six selected products in the AIF 180

5.10 Process plan for the selected products (product information) in the

AIF 182

5.11 Machine-part information for all the six selected products in the AIF 187

5.12 The final manufacturing cells and part families for the four selected

products in AIF 191

5.13 The operation code and operation plan in the RSWF (machine

information) 201

xviii

5.14 Process Plan [Products (Parts) Information] in the RSWF 202

5.15 Machine-part information (8*22) in RSWF 203

5.16 The conversion procedures of the RSWF to cellular manufacturing 204

5.17 The final manufacturing cells and part families in the RSWF 206

6.1 The real life datasets collected from the factories in Malaysia and

Iraq 211

6.2 Basher and Karaa's results that depended on Jaccard measure 213

6.3 The results of the current study of nineteen SCMs 213

6.4 The percentage of agreement with Basher and Karaa’s study 215

6.5 The percentage of differences in the CI between Basher and Karaa’s

study and the current one 215

6.6 Comparison between the mmax and SC based methods 217

6.7 The number of datasets, produced different solutions for each SCM 220

6.8 The results of the discriminability matrix 221

6.9 The results of the stability matrix 223

6.10 The results of the mean and standard deviation of the clustering

tendency index (CI) 224

6.11 Comparison of the current study with Yin and Yasuda's study 225

6.12 The presence and the number of the single machine cells of the

proposed method 228

6.13 The number and the distribution of the machines by using the twenty

SCMs of the proposed method 229

6.14 Comparison the number of the machine cells in the FA and CF of the

proposed method 230

6.15 A comparison of the GC results of the proposed method by using the

twenty SCMs 232

6.16 The best solutions of the proposed method by using the twenty

SCMs and the eight performance measures 232

6.17 The performance of the three proposed methods for integrating the

FA and CF, using three measures 233

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6.18 The matching of theoretical datasets with the assumptions of the

proposed method 237

6.19 The best solutions of the proposed method and AHC by using five

performance measures 238

6.20 The best solutions of the proposed method and AHC by using four

performance measures 239

6.21 Comparison the proposed method solutions with AHC solutions 249

6.22 Comparison the number of cells in the FA and CF of the proposed

method and AHC 250

6.23 The presence of the single machine cells of the proposed method and

AHC 252

6.24 The effect of the existing single machine cells on the GC and IC of

the proposed method and AHC 253

6.25 The number of machine cells with and without using the production

features in the FA phase 257

6.26 The number of machine cells with and without the production

features in the CF phase 259

6.27 The results of implementing the mathematical model, using the GA

for the theoretical datasets 261

6.28 Comparison of the grouping efficacy of the SC method with the

mathematical model solved by GA for the theoretical datasets 261

6.29 The matching of the real life datasets with the assumptions of the

developed method 262

6.30 Integrating Index (II) for the real life datasets 262

6.31 The results of the mathematical model, using the GA for the real life

data 263

6.32 Comparison the GC of the developed SC method with the GA model

for the real life datasets 263

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LIST OF FIGURES

Figure No. Page No.

1.1 Contents of chapter 1 1

1.2 Layout Types (Burbidge, 1971) 6

1.3 Methods of Identifying part families and machine cells (Burbidge,

1971) 7

1.4 The basic structure of the Opitz system (Modak et al., 2011) 10

1.5 Cell formation methods (Albadawi et al., 2005) 12

1.6 Flow chart of the problem statement 17


2.2 (Dis) Similarity coefficient taxonomy (Yin and Yasuda, 2006) 30

2.3 The nearest distance between two objects 37

2.4 Steps of the SLCA (McAuley, 1972) 38

2.5 The longest distance between two objects 39

2.6 The average similarity of the observations between the two clusters 40

2.7 The total “sum of squared” deviations of each object 41

2.8 The idea of the present research 67


3.2 Block diagram representation of the comparison methodology with

Basher and Karaa’s study of the FA 71

3.3 Block diagram representation of the methodology for identifying the

number of machine cells 73

3.4 Block diagram representation of the proposed methodology in the

feasibility assessment 77

3.5 Block diagram representation of using SCMs based on the

production features in the FA 79

3.6 Block diagram representation for calculating the number of machine

cells and clustering tendency index 81

xxi

3.7 Block diagram representation of the proposed methodology for the

CF 83

3.8 Block diagram representation of using the SCMs based on the

production features in the CF 85

3.9 Block diagram representation of methodology for integrating the FA

and CF 86

3.10 Block diagram representation of using the AHC methods in the CF 90

3.11 Block diagram representation of the proposed methodology of

evaluation 92

3.12 The comprehensive research methodology flow chart 94


4.2 The GA flow chart 151

4.3 The Dendrogram by using the AHC for dataset 5*8 156




5.2 The existing functional layout of PRF 166

5.3 The final machine cells design layout in PRF 173

5.4 Some products in AIF 176

5.5 The AIF departments 177

5.6 The existing Functional layout of AIF 178

5.7 The final machine cells design layout for Ditcher 193

5.8 The final machine cells design layout for Chisel Plough 194

5.9 The final machine cells design layout for Spring Cultivator 195

5.10 The final machine cells design layout for Agricultural Tank 196

5.11 The RSWF departments 198

5.12 The existing functional layout of RSWF 199

5.13 Layout of each department in RSWF 200

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5.14 The final machine cells design layout in RSWF 207


6.2 Variation in the size of the machine-part incidence matrix (the

theoretical datasets) 210

6.3 Variation in the size of the machine-part incidence matrix (the real

life datasets) 211

6.4 The number of datasets, produced different solutions for each SCM 221

6.5 The Discriminability and Stability of the SCMs 226

6.6 The mean and standard deviation of the CI of the SCMs 226

6.7 Comparison of the GC obtained by this research and the AHC

approaches 240

6.8 Percent of exceptional elements (PE) of the AHC and proposed

method 241

6.9 Machine utilization values of the proposed and AHC 242

6.10 Grouping capability index of the proposed approach and AHC 244

6.11 Ratio of non zero elements in the cells (REC) of the proposed

method and AHC 245

6.12 Linear performance measure of the proposed method and AHC 246

6.13 Machine utilization index of the proposed method and AHC 247

6.14 Total number of the intercellular (IC) moves of the proposed method

and AHC 248

6.15 Comparison of the II of the proposed method and AHC approaches 249

6.16 The number of cells in the FA and CF of the proposed method and

AHC 251

6.17 The presence of the single machine cell of the proposed method and

AHC 253

6.18 Comparison of the GC with and without the presence of the single

machine cells 254

6.19 Comparison of the GC with and without presence of the single

machine cells 254

6.20 Comparison of the IC moves with and without presence of the single

machine cells 255

xxiii

6.21 Comparison of the IC moves with and without the presence of the

single machine cells 255

6.22 Cost saved after reduction in the total number of the IC moves by

using the proposed method 255

6.23 Cost saved after reduction in the total number of the IC moves by

using the proposed method 256

6.24 The number of machine cells with and without using the production

features in the FA phase 258

6.25 The number of machine cells with and without the production

features in the CF phase 260

xxiv

LIST OF ABBREVIATIONS

AC Ant Colony

ACO Ant Colony Optimization

AHC Agglomerative Hierarchical Clustering

AI Artificial Intelligence

AIF Agricultural Implements Factory

ALCA Average Linkage Clustering Analysis

BE Bond Energy

C&C Classification and Coding

CF Cell formation

CFP Cell formation problems

Chro Chromosome

CI Clustering Tendency Index

CL Cellular Layout

CLCA Complete Linkage Clustering Analysis

CM Cellular manufacturing

CMS Cellular Manufacturing System

CS Cellular Scheduling

DM Drilling Machine

EE Exceptional Element

ES Elite Size

FA Feasibility Assessment

FL Fuzzy Logic

FLCA Flexible Linkage Clustering Analysis

GA Genetic algorithm

GC Grouping Efficacy

GCI Grouping Capability Index

GE Grouping Efficiency

GM Grouping Measure

GPSCs General purpose similarity coefficients

GT Group Technology

HPFOCS Heuristic Part Family by Opitz Coding

System

IC InterCellular moves

LPM Linear Performance Measure

MCs Machine Cells

MGE Modify Grouping Efficiency

MGI Machine groups identification

MINLP Mixed-Integer Non-Linear Programming

MIP Mixed-Integer Programming

ML Machine Layout

MU Machine Utilization

MUI Machine Utilization Index

PCA Principal Component Analysis

PE Percentage of Exceptional Elements

job shop conversion of manufacturing system to …

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