作业车间调度的非合作博弈模型与混合自适应遗传算法

周光辉 1; 王蕊 1; 江平宇 1; 张国海 1; 2

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作业车间调度的非合作博弈模型与混合自适应遗传算法

更新时间：2025-07-09

- 作业车间调度的非合作博弈模型与混合自适应遗传算法
- Non-Cooperation Game Model and Hybrid Adaptive Genetic Algorithm for Job-Shop Scheduling
- 西安交通大学学报 2010年44卷第5期页码：35-39+70
- 作者机构：
  
  西安交通大学机械制造系统工程国家重点实验室,西安,710049
- 作者简介：
- 基金信息：
  
  : 2009-10-11. 作者简介: 周光辉(1972-),男,副教授. 基金项目: 国家自然科学基金资助项目(50605050);国家“863计划”资助项目(2007AA002Z108);西安交通大学机械制造系统工程国家重点实验室开放基金资助项目
- DOI：
  中图分类号： TH166
- 网络首发：2010-05-10，
  
  纸质出版：2010
- 稿件说明：
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周光辉 1, 王蕊 1, 江平宇 1, 等. 作业车间调度的非合作博弈模型与混合自适应遗传算法[J]. 西安交通大学学报, 2010,44(5):35-39+70.

Non-Cooperation Game Model and Hybrid Adaptive Genetic Algorithm for Job-Shop Scheduling[J]. 2010, 44(5): 35-39+70.
周光辉 1, 王蕊 1, 江平宇 1, 等. 作业车间调度的非合作博弈模型与混合自适应遗传算法[J]. 西安交通大学学报, 2010,44(5):35-39+70. DOI：

Non-Cooperation Game Model and Hybrid Adaptive Genetic Algorithm for Job-Shop Scheduling[J]. 2010, 44(5): 35-39+70. DOI：

摘要

采用博弈理论

建立了一种基于非合作博弈的作业车间任务调度模型.在该任务调度模型中

将源于不同客户的制造任务映射为非合作博弈模型中的局中人

并将与制造任务包含的工序集所对应的可选加工设备映射为可行方案集

使各制造任务的加工完成时间和成本组合形成的多目标综合指标映射为收益函数

从而将对任务调度模型的求解转换为寻求非合作博弈模型的Nash均衡点.通过设计的爬山搜索混合自适应遗传算法、自适应交叉和变异算子

实现了对该任务调度非合作博弈模型的Nash均衡点的有效求解

同时算例仿真结果也验证了所提出的调度方法的正确性.

Abstract

To meet the competition requirements of jobs submitted by different customers in job-shop scheduling

taking the maximal profit of each job as the scheduling objective

a non-cooperation game model is proposed. In this job-shop scheduling game model

the players correspond to the jobs submitted by related customers

the strategies of each job correspond to the alternative machines related to operations of this job

and the payoff of each job is defined as the weighted composite of finishing time and cost. Therefore

obtaining the optimal scheduling results is determined by the Nash equilibrium point of this non-cooperation game. To find the Nash equilibrium point efficiently

a hybrid adaptive genetic algorithm based on hill-climbing method is designed as well as the adaptive crossover operator and mutation operator. A numerical case study demonstrates the validity of the job-shop scheduling strategy.

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Keywords

references

JIANG Pingyu, ZHOU Guanghui, ZHAO Gang, et al. E-2-MES: an e-service-driven networked manufacturing platform for extended enterprises [J]. International Journal of Computer Integrated Manufacturing, 2007, 20(2/3): 127-142.

ZHOU Guanghui, JIANG Pingyu, ZHANG Guohai. Game theoretical framework for process plan decision of jobs in networked manufacturing [C]∥Proceedings of the IEEE International Conference on Automation and Logistics. Piscataway, NJ USA: IEEE, 2007: 1868-1873.

SAKAWA M, MORI T. An efficient genetic algorithm for job-shop scheduling problems with fuzzy processing time and fuzzy due date [J]. Computers Industrial Engineering, 1999, 36(2): 325-341.

MATTFELD D C,BIERWIRTH C. An efficient genetic algorithm for job shop scheduling with tardiness objectives [J]. European Journal of Operational Research,2004,155(3): 616-630.

YUN Y S. Genetic algorithm with fuzzy logic controller for preemptive and non-preemptive job-shop scheduling problems [J]. Computers Industry Engineering, 2002, 43(3): 623-644.

KIM B H. A new game-theoretic framework for maintenance strategy analysis [J]. IEEE Transactions on Power Systems, 2003, 18(2): 698-706.

ALI R Y. An effective hybrid immune-hill climbing optimization approach for solving design and manufacturing optimization problems in industry[J]. Journal of Materials Processing Technology, 2009, 209(6): 2773-2780.

汪西原,汪西莉. 启发式搜索策略(爬山法)的改进与实现 [J]. 陕西师范大学学报(自然科学版),1999,27(1):58-65.

WANG Xiyuan, WANG Xili. Improve and implement a heuristic search technique-hill-climbing [J]. Journal of Shaanxi Normal University(Natural Science Edition), 1999,27(1): 58-65.

周光辉,张国海,王蕊,等. 采用实时生产信息的单元制造任务动态调度方法 [J]. 西安交通大学学报,2009,43(11): 56-60.

ZHOU Guanghui, ZHANG Guohai, WANG Rui, et al. Dynamic job scheduling method based on real-time production information for manufacturing cell [J]. Journal of Xi'an Jiaotong University, 2009, 43(11): 56-60.

SRINIVAS M, PATNAIK L M. Adaptive probabilities of crossover and mutation in genetic algorithm [J]. IEEE Trans on Systems, Man and Cybernetics, 1994,24(4):656-667.

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