- 集成式工艺规划与车间调度方法(英文版)(精)
- - 作者：编者:Xinyu Li//Liang Gao
  - 出版社：科学
  - ISBN：9787030756138
  - 出版日期：2023/01/01
  - 页数：462
- 售价：102.4

内容大纲
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个部分共二十一章。第一部分重点对工艺规划、车间调度、柔性作业车间调度以及集成式工艺规划与车间调度等问题的最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。
作者介绍
目录
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

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