集成式工艺规划与车间调度方法
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￥ 153.4 6.0折 ￥ 256 全新
库存64件
广东广州
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作者Xinyu Li，Liang Gao[著]
出版社科学出版社
ISBN9787030756138
出版时间2023-01
装帧精装
开本其他
定价256元
货号12940192
上书时间2023-10-21
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目录
Contents
1 Introduction for Integrated Process Planning and Scheduling 1
1.1 Process Planning 1
1.2 Shop Scheduling 3
1.2.1 Problem Statement 3
1.2.2 Problem Properties 4
1.2.3 Literature Review 5
1.3 Integrated Process Planning and Shop Scheduling 6
References 1
2.Review for Flexible .Job Shop Scheduling 7
2.1 Introduction 17
2.2 Problem Description 8
2.3 The Methods for FISP 18
2.3.1 Exact Algorithms 20
2.3.2 Heuristics 22
2.3.3 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 Scheduling 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
3.2.3 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
4.2.3 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 Scheduling 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 Neighborthood Structure Evaluation Method Based on Logistic Model 117
6.4.1 The Logistic Model 117
6.4.2 Defining Neighbothood 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 Operator 143
7.4.5 Crossover Operator 143
7.4.6 Mutation Operator 145
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 Scheduling 217
11.4 Experimental Studics and Discussion 223
11.4.1 Test Problems and Experimental Results 223
11.4.2 Comparison with Hierarchical Approach 231
11.5 Discussion 232
11.6 Conclusion
References 232
12 An Efective Hybrid Algorithm for IPPS 235
12.1 Hybnd Algorithm Mode 235
12.1.1 Traditionally Genetic Algorithm 235
12.1.2 Local Search Strategy 235
12.1.3.Hybrid Algorithm Model 236
12.2 Hybrid Algorithm for IPPS 237
12.2.1 Encoding and Decoding 237
12.2.2 Initial Population and Fitness Evaluation 239
12.2.3 Genetic Operators for IPPS .239
12.3 Experimental Studies and Discussions 243
12.3.1 Test Problems 243
123.2 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 Scheduling 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 Flexibilities 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 Scheduling 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 2 315
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 323
16.1 Introduction 323
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
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