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 330 16.4.1 Workflow of the Proposed Algorthm 330 16.4.2.Nash Equilibrium Solutions Algorithm for Multi-objective IPPS 331 16.5 Experimental Results 335 16.5.1 Problem 1 335 16.5.2 Problem 2 336 16.5.3 Conclusions 341 References 342 17 A Hybrid Intelligent Algorithm and Rescheduling Technique for Dynamnic JSP 345 17.1 Introduction 345 17.2 Statement of Dynamie JSPs 347 17.2.1 The Proposed Mathematical Model 347 17.2.2 The Reschedule Strategy 350 17.2.3 Generate Real-Time Events 351 17.3 The Proposed Rescheduling Technique for Dynamic JSPs 353 17.3.1 The Rescheduling Technique in General 353 17.3.2 The Hybrid GA and TS for Dynamic JSP 355 17.4 Experiential Environments and Results 360 17.4.1 Experimental Environments 361 17.4.2 Results and Discussion 362 17.5 Conclusions and Future Works 372 18 A Hybrid Genetic Algorithm and Tabu Search for Multi-objective Dynamic JSP 377 18.1 Introduction 377 18.2 Literature Review 378 18.3 The Multi-obective Dynamic Job Shop Scheduling 379 18.4 The Proposed Method for Dynamic JSP 381 18.4.1 The Flow Chart of the Proposed Method 381 18.4.2 Simulator 383 18.4.3 The Hybrid GA and TS for Dynamic JSP 384 18.5 Experimental Design and Rsuls.387 18.5.1 Experimental Design 387 18.5.2 Results and Discussions 388 18.6 Conclusions and Future Researches 400 References .401 19 GEP-Based Reactive Scheduling Policies for DynamicFJSP with Job Release Dates 405 19.1 Introduction 405 19.2 Problem Description 407 19.3 Heuristic for DFISP 408 19.4 GEP Based Reactive Scheduling Polices Constructing Approach 409 19.4.1 Framework of GEP-Based Reactive Scheduling Policies Constructing Approach 409 19.4.2 Define Element Sets 409 19.4.3 Chromosome Representation 411 19.4.4 Genetic Operators 413 19.5 Experiments and Results 414 19.5.1 GEP Parameter Settings 414 19.5.2 Design of the Experiments 414 19.5.3 Analysis of the Results 416 19.6 Conclusion and Future Work 426 References 427 20 A Hybrid Genetic Algorithm with Variable Neighborhood Search for Dynamic IPPS 429 20.1 Introduction 429 20.2 Related Work 430 20.3 Dynamic IPPS Problem Formulation 433 20.3.1 Problem Definition 433 20.3.2 Framework for DIPPS 433 2.3.3 Dynamic IPPS Model 435 20.4 Proposed Hybrid GAVNS for Dynamic IPPS 438 20.4.1 Flowchart of Hybrid GAVNS 438 20.4.2 GA for IPPS 438 20.4.3 VNS for Local Search 440 20.5 Experiments and Discussions 442 20.5.1 Experiment 1 443 20.5.2 Experiment 2 444 20.5.3 Experiment 3 448 20.5.4 Discussion 451 20.6 Conclusion and Future Works 451 References 452 21 IPPS Simulation Prototype System 455 21.1 Application Background Analysis 455 21.2 System Architecture 456 21.3 Implementation and Application 457 21.4 Conclusion 461 References 462
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