Contents
1 Introduction for Integrated Process Planning and Scheng 1
1.1 Process Planning 1
1.2 Shop Scheng 3
1.2.1 Problem Statement 3
1.2.2 Problem Properties 4
1.. Literature Review 5
1.3 Integrated Process Planning and Shop Scheng 6
References 1
2.Review for Flexible .Job Shop Scheng 7
2.1 Introduction 17
2.2 Problem Description 8
. The Methods for FISP 18
..1 Exact Algorithms 20
..2 Heuristics 22
.. Meta-Heuristics 24
2.4 Real-World Applications 33
2.5 Development Trends and Future Research Opportunities 33
2.5.1 Development Trends 33
2.5.2 Future Research Opportunities 34
References 37
3 Review for Integrated Process Planning and Scheng 47
3.1 IPPS in Support of Distributed and Collaborative Manufacturing 47
3.2 Integration Model of IPPS 48
3.2.1 Non-I ,inear Process Planning 48
3.2.2 Closed-Loop Process Planning 49
3.. Distributed Process Planning 50
3.2.4 Comparison of Integration Models 51
3.3 Implementation Approaches of IPPS 52
3.3.1 Agent- Based Approaches of IPPS 52
3.3.2 Petri-Net-Based Approaches of IPPS 54
3.3.3 Algorithm-Based Approaches of IPPS 54
3.3.4 Critique of Curent Implementation Approachs 55
References 56
4 Improved Genetic Programming for Process Planning 61
4.1 Introduction
4.2 Flexible Process Planning 62
4.2.1 Flexible Process Plans 62
4.2.2 Representation of Flexible Process Plans 64
4.. Mathematical Model of Flexible Process Planning 64
4.3 Brief Review of GP 67
4.4 GP for Flexible Process Planning 68
4.4.1 The Flowchart of Proposed Metbod 68
4.4.2 Convert Network to Tree, Encoding, and Decoding 69
4.4.3 Initial Population and Fitness Evaluation 71
4.4.4 GP Operators 72
4.5 Case Studies and Discussion 74
4.5.1 Implementation and Testing 74
4.5.2 Comparison with GA 75
4.6 Conclusion 78
References 78
5 An Efficient Modified Particle Swarm Optimization Algorithm for Process Planning 81
5.1 Introduction 81
5.2 Related Work 82
5.2.1 Process Planning 82
5.2.2 PSO with Its Applications 84
5.3 Problem Formulation 84
5.3.1 Flexible Process Plans 84
5.3.2 Mathematical Model of Process Planning Problem 85
5.4 Modified PSO for Process Planning 86
5.4.1 Modified PSO Model 86
5.4.2 Modified PSO for Process Planning 88
5.5 Experimental Studies and Discussions 94
5.5.1 Case Studies and Results 94
5.5.2 Discussion 102
5.6 Conclusions and Future Research Studics 104
References 104
6 A Hybrid Algorithm for Job Shop Scheng Problem 107
6.1 Introduction 107
6.2 Problem Formulation 110
6.3 Proposed Hybrid Algorithm for JSP 112
6.3.1 Description of the Proposed Hybrid Algorithm 112
6.3.2 Encoding and Decoding Scheme 114
6.3.3 Updating Srace 116
6.3.4 Local Search of the Particle 116
6.4 The Neiorthood Structure Evaluation Method Based on Logistic Model 117
6.4.1 The Logistic Model 117
6.4.2 Defining Neiothood Structures 118
6.4.3 The Evaluation Method Based on Logistic Model 119
6.5 Experiments and Discussion 121
6.5.1 The Search Ability of VNS 121
6.5.2 Benchmark Experiments 122
6.5.3 Convergence Analysis of HPV 124
6.5.4 Discussion 128
6.6 Conclusions and Future Works 128
References 129
7 An Efctive Genetic Algorithm for FJSP 133
7.1 Introduction 133
7.2 Problem Formulation 134
7.3 L ,iterature Review 135
7.4 An Effective GA for FISP 137
7.4.1 Representation 137
7.4.2 Decoding the MSOS Chromosome to a Feasibleand Active Schedule 139
7.4.3 Initial Population 140
7.4.4 Selection Operatr 43
7.4.5 Crossover Operatr 43
7.4.6 Mutation Operatr 45
7.4.7 Framework of the Effective GA 146
7.5 Computational Results 147
7.6 Conclusions and Future Study 149
References 153
8 An Elfective Collaborative Evolutionary Algorithm for FJSP 157
8.1 Initroduction 157
8.2 Problem Formulation 158
Proposed MSCEA for FISP 158
8.3.1 The Optimization Strategy of MSCEA 158
8.3.2 Encoding 159
8.3.3 Initial Population and Fitness Evaluation 160
8.3.4 Genetic Operators 160
8.3.5 Terminate Criteria 161
8.3.6 Framework of MSCEA 161
8.4 Experimental Studies 163
8.5 Conclusions 163
References 165
9 Mathematical Modeling and Evolutionary Algorithum-Based Approach for IPPS 167
9.1 Introduction 167
9.2 Problem Formulation and Mathematical Modeling 168
9.2.1 Problem Formulation 168
9.2.2 Mathematical Modeling 169
9.3 Evolutionary Algorithm-Based Approach for IPPS 173
9.3.1 Representation 173
9.3.2 Initialization and Fitness Evaluation 174
9.3.3 Genetic Operators .174
9.4 Experimental Studies and Discussions 178
9.4.1 Example Problems and Experimental Results 178
9.4.2 Discussions 187
9.5 Conclusion.187
References 188
10 An Agent-Based Approach for IPPS 191
10.1 Literature Survey 191
10.2 Problem Formulation 192
10.3 Proposed Agent-Based Approach for IPPS 195
10.3.1 MAS Architecture 195
10.3.2 Agents Description 195
10.4.Implementation and Experimental Studies 200
10.4.1 System Implenentaion 200
10.42 Experimental Results and Discussion 202
10.4.3 Discussion 205
10.5 Conclusion 205
References 207
11 A Modified Genetic Algorithm Based Approach for IPPS 209
11.1 Integration Model of IPPS 209
11.2 Representations for Process Plans and Schedules 210
11.3 Modified GA-Based Optimization Approach.212
11.3.1 Flowchart of the Proposed Approach 212
11.3.2 Genetic Components for Process Planning 213
11.3.3 Genetic Components for Scheng 217
11.4 Experimental Studics and Discussion 2
11.4.1 Test Problems and Experimental Results 2
11.4.2 Comparison with Hierarchical Approach 1
11.5 Discussion 2
11.6 Conclusion
References 2
12 An Efective Hybrid Algorithm for IPPS 5
12.1 Hybnd Algorithm Mode 5
12.1.1 Traditionally Genetic Algorithm 5
12.1.2 Local Search Strategy 5
12.1.3.Hybrid Algorithm Model
12.2 Hybrid Algorithm for IPPS
12.2.1 Encoding and Decoding
12.2.2 Initial Population and Fitness Evaluation
12.. Genetic Operators for IPPS .
1. Experimental Studies and Discussions 243
1..1 Test Problems 243
1.2 Experimental Results 244
12.4 Discussion 245
12.5 Conclusion 249
References 249
13 An Effective Hybrid Particle Swarm Optimization Algorithm for Multi-objective FJSP 251
13.1 Introduction 251
13.2 Problem Formulation.252
13.3 Particle Swarm Optimization for FISP 255
13.3.1 Traditional PSO Algorithn 255
13.3.2 Tabu Search Strategy 256
13.3.3 Hybrid PSO Algorithm Model 257
13.3.4 Fitness Function 258
13.3.5 Encoding Scheme 259
13.3.6.Information Exchange 261
13.4 Experimental Results 262
13.4.1 Problem 4 x 5 262
13.4.2 Problem 8 x 8 264
13.4.3 Problem 10 x 10 264
13.4.4.Problem 15 x 10 267
13.5 Conclusions and Future Research 276
References 276
14 A Multi- objctive GA Based on Immune and EntropyPrinciple for FJSP 279
14.1 Introduction 279
14.2 Multi-objective Flexible Job Shop Scheng Problem 281
14.3 Basic Concepts of Multi-objective Optimization 283
14.4 Handing MOFISP with MOGA Based on Immune and .Entropy Principle 283
14.4.1 Fitness Assignment Scheme 283
14.4.2 Immune and Entropy Principle 284
14.4.3 Initialization 286
14.4.4 Encoding and Decoding Scheme 286
14.4.5 Selection Operator 287
14.4.6 Crossover Operator 288
14.4.7 Mutation Operator 289
14.4.8 Main Algorithm 290
14.5 Experimental Rcesults 290
14.6 Conclusions 294
References 300
15 An Efective Genetic Algorithm for Multi-objective IPPSwith V arious Flelities in Process Planning 301
15.1 Introduction 301
15.2 Multi-objective IPPS Description 302
15.2.1 IPPS Description 302
15.2.2 Mli-objctive Optimizaion 304
15.3 Proposed Genetic Algorithm for Multi objective IPPS 305
15.3.1 Worktlow of the Proposed Algorithm 305
15.3.2 Genetic Components for Process Planning 307
15.3.3 Genetic Components for Scheng 310
15.3.4 Pareto Set Update Scheme 311
15.4 Experimental Results and Discussions 312
15.4.1 Experiment 1 312
15.4.2.Experiment 15
15.4.3 Discussions 316
15.5 Conclusion and Future Works 321
References 321
16 Application of Game Theory-Based Hybrid Algorithm for Multi-objective IPPS 3
16.1 Introduction 3
16.2 Problem Formulation 325
16.3.Game Theory Model of Muli-objective IPP 328
16.3.1 Game Theory Model of Multi-objective Optimization Problem 328
16.3.2 Nash Equilibrium and MOP 329
16.3.3 Non-cooperative Game Theory for Multi- objective IPPS Proble 329
16.4 Applications of the Proposed Algorithm on Multi-objective IPPS 330
本书总结了作者在集成式工艺规划与车间调度问题上的研究成果，共包含5个部分：部重点对工艺规划、车间调度、柔作业车间调度以及集成式工艺规划与车间调度等问题的近期新研究成果进行了系统的综述；第二部分重点针对单目标的集成式工艺规划与车间调度问题的理论与方法进行系统介绍，提出了该问题的数学模型以及高效优化方法；第三部分重点针对多目标的集成式工艺规划与车间调度问题的理论与方法进行系统介绍，提出了该问题的多目标数学模型以及高效优化及决策方法；第四部分重点针对不确定及动态环境下的集成式工艺规划与车间调度问题的理论与方法进行系统介绍，提出了该问题的数学模型、处理策略以及高效优化方法；第五部分重点针对集成式工艺规划与车间调度问题研究成果的应用进行系统介绍，设计并开发了针对该问题的软件系统，并介绍了该系统的在相关生产车间的应用情况。
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