內容大鋼
本書總結了作者在集成式工藝規劃與車間調度問題上的研究成果，共包含5個部分共二十一章。第一部分重點對工藝規劃、車間調度、柔性作業車間調度以及集成式工藝規劃與車間調度等問題的最新研究成果進行了系統的綜述；第二部分重點針對單目標的集成式工藝規劃與車間調度問題的理論與方法進行系統介紹，提出了該問題的數學模型以及高效優化方法；第三部分重點針對多目標的集成式工藝規劃與車間調度問題的理論與方法進行系統介紹，提出了該問題的多目標數學模型以及高效優化及決策方法；第四部分重點針對不確定及動態環境下的集成式工藝規劃與車間調度問題的理論與方法進行系統介紹，提出了該問題的數學模型、處理策略以及高效優化方法；第五部分重點針對集成式工藝規劃與車間調度問題研究成果的應用進行系統介紹，設計並開發了針對該問題的軟體系統，並介紹了該系統的在相關生產車間的應用情況。
目錄
1 Introduction for Integrated Process Planning and Scheduling
1.1 Process Planning
1.2 Shop Scheduling
1.2.1 Problem Statement
1.2.2 Problem Properties
1.2.3 Literature Review
1.3 Integrated Process Planning and Shop Scheduling
References
2.Review for Flexible Job Shop Scheduling
2.1 Introduction
2.2 Problem Description
2.3 The Methods for FISP
2.3.1 Exact Algorithms
2.3.2 Heuristics
2.3.3 Meta-Heuristics
2.4 Real-World Applications
2.5 Development Trends and Future Research Opportunities
2.5.1 Development Trends
2.5.2 Future Research Opportunities
References
3 Review for Integrated Process Planning and Scheduling
3.1 IPPS in Support of Distributed and Collaborative Manufacturing
3.2 Integration Model of IPPS
3.2.1 Non-I ,inear Process Planning
3.2.2 Closed-Loop Process Planning
3.2.3 Distributed Process Planning
3.2.4 Comparison of Integration Models
3.3 Implementation Approaches of IPPS
3.3.1 Agent- Based Approaches of IPPS
3.3.2 Petri-Net-Based Approaches of IPPS
3.3.3 Algorithm-Based Approaches of IPPS
3.3.4 Critique of Curent Implementation Approachs
References
4 Improved Genetic Programming for Process Planning
4.1 Introduction
4.2 Flexible Process Planning
4.2.1 Flexible Process Plans
4.2.2 Representation of Flexible Process Plans
4.2.3 Mathematical Model of Flexible Process Planning
4.3 Brief Review of GP
4.4 GP for Flexible Process Planning
4.4.1 The Flowchart of Proposed Metbod
4.4.2 Convert Network to Tree, Encoding, and Decoding
4.4.3 Initial Population and Fitness Evaluation
4.4.4 GP Operators
4.5 Case Studies and Discussion
4.5.1 Implementation and Testing
4.5.2 Comparison with GA
4.6 Conclusion
References
5 An Efficient Modified Particle Swarm Optimization Algorithm for Process Planning
5.1 Introduction
5.2 Related Work
5.2.1 Process Planning
5.2.2 PSO with Its Applications
5.3 Problem Formulation
5.3.1 Flexible Process Plans
5.3.2 Mathematical Model of Process Planning Problem
5.4 Modified PSO for Process Planning
5.4.1 Modified PSO Model
5.4.2 Modified PSO for Process Planning
5.5 Experimental Studies and Discussions
5.5.1 Case Studies and Results
5.5.2 Discussion
5.6 Conclusions and Future Research Studics
References
6 A Hybrid Algorithm for Job Shop Scheduling Problem
6.1 Introduction
6.2 Problem Formulation
6.3 Proposed Hybrid Algorithm for JSP
6.3.1 Description of the Proposed Hybrid Algorithm
6.3.2 Encoding and Decoding Scheme
6.3.3 Updating Srace
6.3.4 Local Search of the Particle
6.4 The Neighborthood Structure Evaluation Method Based on Logistic Model
6.4.1 The Logistic Model
6.4.2 Defining Neighbothood Structures
6.4.3 The Evaluation Method Based on Logistic Model
6.5 Experiments and Discussion
6.5.1 The Search Ability of VNS
6.5.2 Benchmark Experiments
6.5.3 Convergence Analysis of HPV
6.5.4 Discussion
6.6 Conclusions and Future Works
References
7 An Efctive Genetic Algorithm for FJSP
7.1 Introduction
7.2 Problem Formulation
7.3 L ,iterature Review
7.4 An Effective GA for FISP
7.4.1 Representation
7.4.2 Decoding the MSOS Chromosome to a Feasibleand Active Schedule
7.4.3 Initial Population
7.4.4 Selection Operator
7.4.5 Crossover Operator
7.4.6 Mutation Operator
7.4.7 Framework of the Effective GA
7.5 Computational Results
7.6 Conclusions and Future Study
References
8 An Elfective Collaborative Evolutionary Algorithm for FJSP
8.1 Initroduction
8.2 Problem Formulation
Proposed MSCEA for FISP
8.3.1 The Optimization Strategy of MSCEA
8.3.2 Encoding
8.3.3 Initial Population and Fitness Evaluation
8.3.4 Genetic Operators
8.3.5 Terminate Criteria
8.3.6 Framework of MSCEA
8.4 Experimental Studies
8.5 Conclusions
References
9 Mathematical Modeling and Evolutionary Algorithum-Based Approach for IPPS
9.1 Introduction
9.2 Problem Formulation and Mathematical Modeling
9.2.1 Problem Formulation
9.2.2 Mathematical Modeling
9.3 Evolutionary Algorithm-Based Approach for IPPS
9.3.1 Representation
9.3.2 Initialization and Fitness Evaluation
9.3.3 Genetic Operators .
9.4 Experimental Studies and Discussions
9.4.1 Example Problems and Experimental Results
9.4.2 Discussions
9.5 Conclusion.
References
10 An Agent-Based Approach for IPPS
10.1 Literature Survey
10.2 Problem Formulation
10.3 Proposed Agent-Based Approach for IPPS
10.3.1 MAS Architecture
10.3.2 Agents Description
10.4 .Implementation and Experimental Studies
10.4.1 System Implenentaion
10.42 Experimental Results and Discussion
10.4.3 Discussion
10.5 Conclusion
References
11 A Modified Genetic Algorithm Based Approach for IPPS
11.1 Integration Model of IPPS
11.2 Representations for Process Plans and Schedules
11.3 Modified GA-Based Optimization Approach.
11.3.1 Flowchart of the Proposed Approach
11.3.2 Genetic Components for Process Planning
11.3.3 Genetic Components for Scheduling
11.4 Experimental Studics and Discussion
11.4.1 Test Problems and Experimental Results
11.4.2 Comparison with Hierarchical Approach
11.5 Discussion
11.6 Conclusion
References
12 An Efective Hybrid Algorithm for IPPS
12.1 Hybnd Algorithm Mode
12.1.1 Traditionally Genetic Algorithm
12.1.2 Local Search Strategy
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
12.2.3 Genetic Operators for IPPS .
12.3 Experimental Studies and Discussions
12.3.1 Test Problems
123.2 Experimental Results
12.4 Discussion
12.5 Conclusion
References
13 An Effective Hybrid Particle Swarm Optimization Algorithm for Multi-objective FJSP
13.1 Introduction
13.
13.3 Particle Swarm Optimization for FISP
13.3.1 Traditional PSO Algorithn
13.3.2 Tabu Search Strategy
13.3.3 Hybrid PSO Algorithm Model
13.3.4 Fitness Function
13.3.5 Encoding Scheme
13.3.6 .Information Exchange
13.4 Experimental Results
13.4.1 Problem 4 x
13.4.2 Problem 8 x
13.4.3 Problem 10 x
13.4.4 .Problem 15 x
13.5 Conclusions and Future Research
References
14 A Multi- objctive GA Based on Immune and EntropyPrinciple for FJSP
14.1 Introduction
14.2 Multi-objective Flexible Job Shop Scheduling Problem
14.3 Basic Concepts of Multi-objective Optimization
14.4 Handing MOFISP with MOGA Based on Immune and .Entropy Principle
14.4.1 Fitness Assignment Scheme
14.4.2 Immune and Entropy Principle
14.4.3 Initialization
14.4.4 Encoding and Decoding Scheme
14.4.5 Selection Operator
14.4.6 Crossover Operator
14.4.7 Mutation Operator
14.4.8 Main Algorithm
14.5 Experimental Rcesults
14.6 Conclusions
References
15 An Efective Genetic Algorithm for Multi-objective IPPSwith V arious Flexibilities in Process Planning
15.1 Introduction
15.2 Multi-objective IPPS Description
15.2.1 IPPS Description
15.2.2 Mli-objctive Optimizaion
15.3 Proposed Genetic Algorithm for Multi objective IPPS
15.3.1 Worktlow of the Proposed Algorithm
15.3.2 Genetic Components for Process Planning
15.3.3 Genetic Components for Scheduling
15.3.4 Pareto Set Update Scheme
15.4 Experimental Results and Discussions
15.4.1 Experiment
15.4.2 .Experiment
15.4.3 Discussions
15.5 Conclusion and Future Works
References
16 Application of Game Theory-Based Hybrid Algorithm for Multi-objective IPPS
16.1 Introduction
16.2 Problem Formulation
16.3 .Game Theory Model of Muli-objective IPP
16.3.1 Game Theory Model of Multi-objective Optimization Problem
16.3.2 Nash Equilibrium and MOP
16.3.3 Non-cooperative Game Theory for Multi- objective IPPS Proble
16.4 Applications of the Proposed Algorithm on Multi-objective IPPS
16.4.1 Workflow of the Proposed Algorthm
16.4.2 .Nash Equilibrium Solutions Algorithm for Multi-objective IPPS
16.5 Experimental Results
16.5.1 Problem
16.5.2 Problem
16.5.3 Conclusions
References
17 A Hybrid Intelligent Algorithm and Rescheduling Technique for Dynamnic JSP
17.1 Introduction
17.2 Statement of Dynamie JSPs
17.2.1 The Proposed Mathematical Model
17.2.2 The Reschedule Strategy
17.2.3 Generate Real-Time Events
17.3 The Proposed Rescheduling Technique for Dynamic JSPs
17.3.1 The Rescheduling Technique in General
17.3.2 The Hybrid GA and TS for Dynamic JSP
17.4 Experiential Environments and Results
17.4.1 Experimental Environments
17.4.2 Results and Discussion
17.5 Conclusions and Future Works
18 A Hybrid Genetic Algorithm and Tabu Search for Multi-objective Dynamic JSP
18.1 Introduction
18.2 Literature Review
18.3 The Multi-obective Dynamic Job Shop Scheduling
18.4 The Proposed Method for Dynamic JSP
18.4.1 The Flow Chart of the Proposed Method
18.4.2 Simulator
18.4.3 The Hybrid GA and TS for Dynamic JSP
18.5 Experimental Design and Rsuls.
18.5.1 Experimental Design
18.5.2 Results and Discussions
18.6 Conclusions and Future Researches
References .
19 GEP-Based Reactive Scheduling Policies for DynamicFJSP with Job Release Dates
19.1 Introduction
19.2 Problem Description
19.3 Heuristic for DFISP
19.4 GEP Based Reactive Scheduling Polices Constructing Approach
19.4.1 Framework of GEP-Based Reactive Scheduling Policies Constructing Approach
19.4.2 Define Element Sets
19.4.3 Chromosome Representation
19.4.4 Genetic Operators
19.5 Experiments and Results
19.5.1 GEP Parameter Settings
19.5.2 Design of the Experiments
19.5.3 Analysis of the Results
19.6 Conclusion and Future Work
References
20 A Hybrid Genetic Algorithm with Variable Neighborhood Search for Dynamic IPPS
20.1 Introduction
20.2 Related Work
20.3 Dynamic IPPS Problem Formulation
20.3.1 Problem Definition
20.3.2 Framework for DIPPS
2.3.3 Dynamic IPPS Model
20.4 Proposed Hybrid GAVNS for Dynamic IPPS
20.4.1 Flowchart of Hybrid GAVNS
20.4.2 GA for IPPS
20.4.3 VNS for Local Search
20.5 Experiments and Discussions
20.5.1 Experiment
20.5.2 Experiment
20.5.3 Experiment
20.5.4 Discussion
20.6 Conclusion and Future Works
References
21 IPPS Simulation Prototype System
21.1 Application Background Analysis
21.2 System Architecture
21.3 Implementation and Application
21.4 Conclusion
References
[