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求解总拖期时间最小化流水车间调度问题的多智能体进化算法

王大志 刘士新 郭希旺

王大志, 刘士新, 郭希旺. 求解总拖期时间最小化流水车间调度问题的多智能体进化算法. 自动化学报, 2014, 40(3): 548-555. doi: 10.3724/SP.J.1004.2014.00548
引用本文: 王大志, 刘士新, 郭希旺. 求解总拖期时间最小化流水车间调度问题的多智能体进化算法. 自动化学报, 2014, 40(3): 548-555. doi: 10.3724/SP.J.1004.2014.00548
WANG Da-Zhi, LIU Shi-Xin, GUO Xi-Wang. A Multi-agent Evolutionary Algorithm for Solving Total Tardiness Permutation Flow-shop Scheduling Problem. ACTA AUTOMATICA SINICA, 2014, 40(3): 548-555. doi: 10.3724/SP.J.1004.2014.00548
Citation: WANG Da-Zhi, LIU Shi-Xin, GUO Xi-Wang. A Multi-agent Evolutionary Algorithm for Solving Total Tardiness Permutation Flow-shop Scheduling Problem. ACTA AUTOMATICA SINICA, 2014, 40(3): 548-555. doi: 10.3724/SP.J.1004.2014.00548

求解总拖期时间最小化流水车间调度问题的多智能体进化算法

doi: 10.3724/SP.J.1004.2014.00548
基金项目: 

国家自然科学基金(61333006,71171038),中央高校基本科研业务费(N110404024)资助

详细信息
    作者简介:

    刘士新 东北大学信息科学与工程学院系统工程研究所教授. 主要研究方向为项目管理, 生产计划与调度, 最优化理论与应用.E-mail:sxliu@mail.neu.edu.cn

    通讯作者:

    王大志

A Multi-agent Evolutionary Algorithm for Solving Total Tardiness Permutation Flow-shop Scheduling Problem

Funds: 

Supported by National Natural Science Foundation of China (61333006, 71171038), the Fundamental Research Funds for the Central Universities (N110404024)

  • 摘要: 针对总拖期时间最小化的置换流水车间调度问题(Total tardiness permutation flow-shop scheduling problem) 提出了一种基于多智能体的进化搜索算法. 在该算法中,采用基于延迟时间排序的学习搜索策略(Tardiness rank based learning),快速产生高质量的新个体,并根据概率更新模型进行智能体网格的更新进化. 同时通过实验设计的方法探讨了算法参数设置对算法性能的影响. 为了验证算法的性能,求解了Vallada标准测试集中540个测试问题,并将测试结果与一些代表算法进行比较,验证了该算法的有效性.
  • [1] Vallada E, Rubn R. Cooperative metaheuristics for the permutation flowshop scheduling problem. European Journal of Operational Research, 2009, 193(2): 365-376
    [2] [2] Vallada E, Rubn R, Gerardo M. Minimising total tardiness in the m-machine flowshop problem: a review and evaluation of heuristics and metaheuristics. Computers Operations Research, 2008, 35(4): 1350-1373
    [3] [3] Liao J M, Huang C J. Tabu search for non-permutation flowshop scheduling problem with minimizing total tardiness. Applied Mathematics and Computation, 2010, 217(2): 557-567
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    [8] Jiao Li-Cheng, Liu Jing, Zhong Wei-Cai, Coevolutionary Computation and Multiagent Systems. Beijng: Science Press, 2006. 205-224(焦李成, 刘静, 钟伟才. 协同进化计算与多智能体系统. 北京: 科学出版社, 2006. 205-224)
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    [11] Sttzle T. Applying iterated local search to the permutation flow shop problem, Technical Report, AIDA-98-04, FG Intellektik, FB Informatik, TU Darmstadt, 1998
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    [16] Hasija S, Rajendran C. Scheduling in flowshops to minimize total tardiness of jobs. International Journal of Production Research, 2004, 42(11): 2289-2301
    [17] Vallada E, Rubn R. Genetic algorithms with path relinking for the minimum tardiness permutation flowshop problem. Omega, 2010, 38(1-2): 57-67
    [18] Ruiz R, Sttzle T. A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. European Journal of Operational Research, 2007, 177(3): 2033-2049
    [19] Zemel E. Measuring the quality of approximate solutions to zero-one programming problems. Mathematics of Operations Research, 1981, 6(3): 319-332
    [20] Kim Y D. Heuristics for flowshop scheduling problems minimizing mean tardiness. Journal of the Operational Research Society, 1993, 44(1): 19-28
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出版历程
  • 收稿日期:  2012-12-12
  • 修回日期:  2013-04-19
  • 刊出日期:  2014-03-20

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