基于SPEA的多目标柔性作业车间调度方法

中国机械工程 ›› 2010, Vol. 21 ›› Issue (10): 1167-1172.

基于SPEA的多目标柔性作业车间调度方法

王云1;谭建荣2;冯毅雄1;李中凯1

1.浙江大学流体传动及控制国家重点实验室,杭州,310027

2.浙江大学CAD&CG国家重点实验室,杭州,310027

出版日期:2010-05-25 发布日期:2010-06-02
基金资助:
国家863高技术研究发展计划资助项目(2007AA04Z190)；国家自然科学基金资助项目(50505044，60573175)
National High-tech R&D Program of China (863 Program) （No. 2007AA04Z190）;
National Natural Science Foundation of China（No. 50505044，60573175）

Multi-objective Flexible Job-shop Scheduling Based on Strength Pareto Evolutionary Algorithm

Wang Yun1;Tan Jianrong2;Feng Yixiong1;Li Zhongkai1

1.State Key Laboratory of Fluid Power Transmission and Control, Zhejiang University, Hangzhou, 310027

2.State Key Laboratory of CAD&CG,Zhejiang University, Hangzhou, 310027

Online:2010-05-25 Published:2010-06-02
Supported by:

National High-tech R&D Program of China (863 Program) （No. 2007AA04Z190）;
National Natural Science Foundation of China（No. 50505044，60573175）

摘要/Abstract

摘要：

研究了多目标柔性作业车间调度问题,构建了以制造工期、加工成本及交货期为目标函数的柔性作业车间多目标调度模型,应用改进的强度Pareto进化算法（SPEA）进行求解。在该算法中,引入模糊C-均值聚类（FCM）加快外部种群的聚类过程。采用约束Pareto支配和双层编码策略,一次运行就能够求得Pareto最优解集,并利用模糊集合理论的方法得到Pareto解的优先选择序列和选出一个最优解。最后,将该方法应用于某机械公司车间调度中,验证了该方法的有效性和适应性。

关键词:

柔性车间调度问题, 多目标优化, SPEA, 多目标决策方法

Abstract:

To solve FJSP, a multi-objective FJSP optimization model was set up, concerned with time, cost and delivery satisfaction. The optimal solutions were obtained by using improved SPEA. The SPEA was improved by introducing the fuzzy C-means clustering algorithm to accelerate the clustering procedure within the external population. With the constraint Pareto domination concept and the two-level representation schema, a Pareto optimal set could be achieved in a single run. Then the preference sequence of Pareto solutions was achieved and a solution was extracted as the best compromise one based on set theory. The feasibility and validity of the proposed algorithm have been proved by the results in a workshop scheduling.  flexible job-shop

Key words: scheduling problem(FJSP), multi-objective optimization, strength Pareto evolutionary algorithm(SPEA), multi-objective decision making method

中图分类号:

TH166

王云, 谭建荣, 冯毅雄, 李中凯.

基于SPEA的多目标柔性作业车间调度方法

[J]. 中国机械工程, 2010, 21(10): 1167-1172.

WANG Yun, TAN Jian-Rong, FENG Yi-Xiong, LI Zhong-Kai.

Multi-objective Flexible Job-shop Scheduling Based on Strength Pareto Evolutionary Algorithm

[J]. China Mechanical Engineering, 2010, 21(10): 1167-1172.

参考文献

[1]Xing Lining, Chen Yingwu, Yang Kewei. Multi- objective Flexible Job Shop Schedule: Design and Evaluation by Simulation Modeling [J]. Applied Soft Computing, 2009,9 (1) : 362-376.
[2]袁坤[1,2],朱剑英[1].一种求解多目标柔性Job Shop调度的改进遗传算法[J].中国机械工程,2007,18(2):156-160.
[3]Gao Jie, Gen Mitsuo, Sun Linyan,et al. A Hybrid of Genetic Algorithm and Bottleneck Shifting for Multiobjective Flexible Job Shop Scheduling Problems[J]. Computers & Industrial Engineering, 2007,53(1) :149-162.
[4]Zhang Guohui, Shao Xinyu, Li Peigen, et al. An Effective Hybrid Particle Swarm Optimization Al- gorithm for Multiobjective Flexible Jobshop Scheduling Problem[J]. Computers &Industrial Engineering, 2009,56(4): 1309-1318.
[5]Xia Weijun, Wu Zhiming. An Effective Hybrid Optimization Approach for Multiobjective Flexible Job-- shop Scheduling Problems[J]. Computers Industrial Engineering, 2005,48 (2) : 409-425.
[6]Zitzler E, Thiele L. Multi--objective Evolutionary Algorithms. a Comparative Case Study and the Strength Pareto Approach[J]. IEEE Trans. Evol. Comput. , 1999, 3(4): 257-271.
[7]Yager R. Intelligent Control of the Hierarchical Agglomerative Clustering Process [J]. IEEE Trans. Syst. , 2000, 30(6): 835-845.
[8]Nikhil R, Kuhu P, keler M, et al. A Possiblistic Fuzzy C-means Clustering Algorithm[J].IEEE Transactions on Fuzzy Systems, 2005, 13(4) : 517- 530.
[9]冯士刚[1],艾芊[1].火电站多目标负荷调度及其算法的研究[J].动力工程,2008,28(3):404-407.
[10]吴作顺 [1],王石 [2].一个SPEA改进算法及其收敛性分析[J].计算机科学,2005,32(4):74-76.
[11]Drik B, Peter S, Rolf d, et al. Multi--objective Evolutionary Algorithm for the Optimization of Noisy Combustion Process[J]. IEEE Transactions on Systems, Man and Cybernetics, 2002, 32(4): 460-473.
[12]余琦玮[1],赵亮[1],潘双夏[1].基于遗传算法的柔性作业车间调度优化[J].组合机床与自动化加工技术,2004(4):32-34.
[13]吴秀丽[1],孙树栋[1],余建军[1],蔡志强[1].多目标柔性作业车间调度决策精选机制研究[J].中国机械工程,2007,18(2):161-165.

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