Intelligent Scheduling of Robotic Flexible Assembly Cells (eBook)

Parts of this thesis have been published in the following documents: 6
Journals 6
International Conferences 6
Book Chapters 7
Supervisor's Foreword 8
Acknowledgments 10
Contents 12
Abbreviations 16
1 Background and Research Scope 18
Abstract 18
1.1 Introduction 18
1.2 Flexible Assembly Systems 19
1.2.1 Robots 19
1.2.2 Peripheral Equipment 21
1.3 Classification of Flexible Assembly Systems 21
1.3.1 Robotic Assembly Line 21
1.3.2 Robotic Assembly Cell 22
1.3.3 Simple Comparison Between RAL and RAC 23
1.4 Scheduling of Robotic Flexible Assembly Cell 24
1.5 Motivation for Research in Scheduling of RFAC 25
1.6 Research Gap and Scope 26
1.7 Concluding Remarks 27
References 27
2 Literature Review and Research Objectives 30
2.1 Introduction 30
2.2 Scheduling Problems in Manufacturing Systems 31
2.2.1 Types of Scheduling Problems 31
2.2.2 Characteristics of Scheduling Problems 32
2.2.3 Solution Approaches 33
2.3 Review of Literature on Advanced Scheduling Approaches 34
2.3.1 Simulation Approaches 34
2.3.2 Artificial Intelligence Approaches 35
2.3.3 Observations from the Literature Review 36
2.4 Scheduling of RFAC: A Literature Review 37
2.4.1 Traditional Approaches 37
2.4.2 Simulation Approaches 38
2.4.3 Expert System Approaches 39
2.5 Research Limitations 39
2.6 Research Objectives and Thesis Plan 40
2.6.1 Research Objectives 40
2.6.2 Research Plan and Thesis Structure 42
2.7 Concluding Remarks 45
References 45
3 Development of an Intelligent Methodology for Scheduling RFAC 48
3.1 Introduction 48
3.2 Application of Fuzzy Logic to Scheduling Problems 48
3.3 Proposed Methodology for Scheduling of RFAC 51
3.3.1 Pre-processing Module 52
3.3.2 Scheduling Module 56
3.3.3 Linguistic Variables 59
3.3.4 Membership Functions 60
3.3.5 Fuzzy Rules 60
3.3.5.1 Simulation Module 61
3.4 Concluding Remarks 62
References 63
4 Case Study 1: Application of the Developed Methodology Using Fuzzy Logic and Simulation 65
4.1 Introduction 65
4.2 A Fuzzy Logic Model for Scheduling RFAC 65
4.2.1 Defining the Linguistic Variables 66
4.2.2 Constructing Membership Functions 66
4.2.3 Constructing Fuzzy Rules 68
4.3 Implementation of Fuzzy Approach for Scheduling of RFAC 69
4.4 Example Application of Scheduling RFAC 73
4.4.1 RFAC Description 73
4.4.2 Simulation of Experimental Design 76
4.5 Simulation Results and Discussion 78
4.6 Concluding Remarks 82
References 83
5 Simulation Modelling and Analysis of Dynamic Scheduling in RFAC 84
5.1 Introduction 84
5.2 Review of Literature on Dynamic Events 85
5.3 A Framework for Developing an Intelligent Approach to Dynamic Scheduling Problems 87
5.3.1 Preparation 88
5.3.2 Application of Taguchi Method 89
5.3.3 Simulation Modelling 89
5.3.4 Statistical Analysis 90
5.4 The Simulation Model 92
5.5 Experimental Design and Results 95
5.5.1 Experimental Setup 95
5.5.2 Taguchi's Orthogonal Array Selection 97
5.5.3 Calculation of the Signal-to-Noise (S/N) Ratio 98
5.6 Analysis of Results and Discussion 98
5.6.1 Analysis of Mean (ANOM) 98
5.6.2 Analysis of Variance (ANOVA) 101
5.7 Concluding Remarks 104
References 105
6 Development of an Optimization Approach for Dynamic Scheduling Problems in RFAC 108
6.1 Introduction 108
6.2 Multi-criteria Decision-Making 109
6.2.1 MCDM Process 109
6.2.2 In Search of a Powerful Method for Complex Decision Making Problems 110
6.3 A Hybrid Approach for Optimization of Dynamic Scheduling Problems 113
6.3.1 Problem Description 114
6.3.2 Application of Fuzzy MCDM Methods 115
6.3.3 Analysis of the Results 116
6.4 Implementation of Fuzzy Decision Support System 117
6.4.1 Structure of Fuzzy Decision Support System 117
6.4.2 Design of the Proposed Fuzzy Decision Support System 120
6.5 Implementation of Fuzzy AHP-Fuzzy TOPSIS 123
6.5.1 Methodology of FAHP 123
6.5.2 Methodology of FTOPSIS 126
6.6 Concluding Remarks 128
References 130
7 Case Study 2: Application of Hybrid Fuzzy MCDM Approach to Optimize Dynamic Scheduling in RFAC 135
7.1 Introduction 135
7.2 Case Study 136
7.3 Application Using FDSS 137
7.3.1 Defining Input and Output Variables 138
7.3.2 Specifying Input and Output Membership Functions 138
7.3.3 Constructing Decision Rules and Knowledge Base 139
7.3.4 Determining MPCI by Using Defuzzification 141
7.4 Application Using FAHP-FTOPSIS 143
7.4.1 Application of FAHP in Determining Weights of Criteria 143
7.4.2 Application of FTOPSIS in Ranking of Alternatives 146
7.5 Analysis of Results and Discussion 148
7.5.1 Comparison of the Results 148
7.5.2 Sensitivity Analysis 149
7.5.3 Effect of Scheduling Factors on MPCI 153
7.5.4 Confirmation Test 153
7.6 Concluding Remarks 154
8 Conclusions and Recommendations for Future Work 156
8.1 Introduction 156
8.2 Summary of the Research 157
8.2.1 Scheduling RFAC in a Multi-product Assembly Environment 157
8.2.2 Scheduling RFAC in a Dynamic Situation 158
8.2.3 Scheduling RFAC in Multi-objective Optimization Problems 158
8.3 Conclusions 159
8.4 Recommendations for Future Work 160
8.4.1 Robust Scheduling of RFAC with Interruptions 160
8.4.2 Virtual Reality for RFAC Simulation 161
8.4.3 Deadlock Prevention and Avoidance in RFAC 162
8.5 Final Word 162
References 163
Appendix A 164
Appendix B 166
Appendix C 168
Appendix D 171
Curriculum Vitae 174