Contents1 Introduction for Integrated Process Planning and Scheduling 11.1 Process Planning 11.2 Shop Scheduling 31.2.1 Problem Statement 31.2.2 Problem Properties 41.2.3 Literature Review 51.3 Integrated Process Planning and Shop Scheduling 6References 12.Review for Flexible .Job Shop Scheduling 72.1 Introduction 172.2 Problem Description 82.3 The Methods for FISP 182.3.1 Exact Algorithms 202.3.2 Heuristics 222.3.3 Meta-Heuristics 242.4 Real-World Applications 332.5 Development Trends and Future Research Opportunities 332.5.1 Development Trends 332.5.2 Future Research Opportunities 34References 373 Review for Integrated Process Planning and Scheduling 473.1 IPPS in Support of Distributed and Collaborative Manufacturing 473.2 Integration Model of IPPS 483.2.1 Non-I ��inear Process Planning 483.2.2 Closed-Loop Process Planning 493.2.3 Distributed Process Planning 503.2.4 Comparison of Integration Models 513.3 Implementation Approaches of IPPS 523.3.1 Agent- Based Approaches of IPPS 523.3.2 Petri-Net-Based Approaches of IPPS 543.3.3 Algorithm-Based Approaches of IPPS 543.3.4 Critique of Curent Implementation Approachs 55References 564 Improved Genetic Programming for Process Planning 614.1 Introduction4.2 Flexible Process Planning 624.2.1 Flexible Process Plans 624.2.2 Representation of Flexible Process Plans 644.2.3 Mathematical Model of Flexible Process Planning 644.3 Brief Review of GP 674.4 GP for Flexible Process Planning 684.4.1 The Flowchart of Proposed Metbod 684.4.2 Convert Network to Tree�� Encoding�� and Decoding 694.4.3 Initial Population and Fitness Evaluation 714.4.4 GP Operators 724.5 Case Studies and Discussion 744.5.1 Implementation and Testing 744.5.2 Comparison with GA 754.6 Conclusion 78References 785 An Efficient Modified Particle Swarm Optimization Algorithm for Process Planning 815.1 Introduction 815.2 Related Work 825.2.1 Process Planning 825.2.2 PSO with Its Applications 845.3 Problem Formulation 845.3.1 Flexible Process Plans 845.3.2 Mathematical Model of Process Planning Problem 855.4 Modified PSO for Process Planning 865.4.1 Modified PSO Model 865.4.2 Modified PSO for Process Planning 885.5 Experimental Studies and Discussions 945.5.1 Case Studies and Results 945.5.2 Discussion 1025.6 Conclusions and Future Research Studics 104References 1046 A Hybrid Algorithm for Job Shop Scheduling Problem 1076.1 Introduction 1076.2 Problem Formulation 1106.3 Proposed Hybrid Algorithm for JSP 1126.3.1 Description of the Proposed Hybrid Algorithm 1126.3.2 Encoding and Decoding Scheme 1146.3.3 Updating Srace 1166.3.4 Local Search of the Particle 1166.4 The Neighborthood Structure Evaluation Method Based on Logistic Model 1176.4.1 The Logistic Model 1176.4.2 Defining Neighbothood Structures 1186.4.3 The Evaluation Method Based on Logistic Model 1196.5 Experiments and Discussion 1216.5.1 The Search Ability of VNS 1216.5.2 Benchmark Experiments 1226.5.3 Convergence Analysis of HPV 1246.5.4 Discussion 1286.6 Conclusions and Future Works 128References 1297 An Efctive Genetic Algorithm for FJSP 1337.1 Introduction 1337.2 Problem Formulation 1347.3 L ��iterature Review 1357.4 An Effective GA for FISP 1377.4.1 Representation 1377.4.2 Decoding the MSOS Chromosome to a Feasibleand Active Schedule 1397.4.3 Initial Population 1407.4.4 Selection Operator 1437.4.5 Crossover Operator 1437.4.6 Mutation Operator 1457.4.7 Framework of the Effective GA 1467.5 Computational Results 1477.6 Conclusions and Future Study 149References 1538 An Elfective Collaborative Evolutionary Algorithm for FJSP 1578.1 Initroduction 1578.2 Problem Formulation 158Proposed MSCEA for FISP 1588.3.1 The Optimization Strategy of MSCEA 1588.3.2 Encoding 1598.3.3 Initial Population and Fitness Evaluation 1608.3.4 Genetic Operators 1608.3.5 Terminate Criteria 1618.3.6 Framework of MSCEA 1618.4 Experimental Studies 1638.5 Conclusions 163References 1659 Mathematical Modeling and Evolutionary Algorithum-Based Approach for IPPS 1679.1 Introduction 1679.2 Problem Formulation and Mathematical Modeling 1689.2.1 Problem Formulation 1689.2.2 Mathematical Modeling 1699.3 Evolutionary Algorithm-Based Approach for IPPS 1739.3.1 Representation 1739.3.2 Initialization and Fitness Evaluation 1749.3.3 Genetic Operators .1749.4 Experimental Studies and Discussions 1789.4.1 Example Problems and Experimental Results 1789.4.2 Discussions 1879.5 Conclusion.187References 18810 An Agent-Based Approach for IPPS 19110.1 Literature Survey 19110.2 Problem Formulation 19210.3 Proposed Agent-Based Approach for IPPS 19510.3.1 MAS Architecture 19510.3.2 Agents Description 19510.4.Implementation and Experimental Studies 20010.4.1 System Implenentaion 20010.42 Experimental Results and Discussion 20210.4.3 Discussion 20510.5 Conclusion 205References 20711 A Modified Genetic Algorithm Based Approach for IPPS 20911.1 Integration Model of IPPS 20