2.765

2022影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

考虑运输能力限制的跨单元调度方法

刘兆赫 李冬妮 王乐衡 田云娜

刘兆赫, 李冬妮, 王乐衡, 田云娜. 考虑运输能力限制的跨单元调度方法. 自动化学报, 2015, 41(5): 885-898. doi: 10.16383/j.aas.2015.c140498
引用本文: 刘兆赫, 李冬妮, 王乐衡, 田云娜. 考虑运输能力限制的跨单元调度方法. 自动化学报, 2015, 41(5): 885-898. doi: 10.16383/j.aas.2015.c140498
LIU Zhao-He, LI Dong-Ni, WANG Le-Heng, TIAN Yun-Na. An Inter-cell Scheduling Approach Considering Transportation Capacity Constraints. ACTA AUTOMATICA SINICA, 2015, 41(5): 885-898. doi: 10.16383/j.aas.2015.c140498
Citation: LIU Zhao-He, LI Dong-Ni, WANG Le-Heng, TIAN Yun-Na. An Inter-cell Scheduling Approach Considering Transportation Capacity Constraints. ACTA AUTOMATICA SINICA, 2015, 41(5): 885-898. doi: 10.16383/j.aas.2015.c140498

考虑运输能力限制的跨单元调度方法

doi: 10.16383/j.aas.2015.c140498
基金项目: 

国家自然科学基金(71401014),北京市自然科学基金(4122069)资助

详细信息
    作者简介:

    刘兆赫 北京理工大学计算机学院硕士研究生. 主要研究方向为演化计算和生产调度. E-mail: cyuyan888@163.com

    通讯作者:

    李冬妮 北京理工大学计算机学院副教授. 主要研究方向为智能优化, 企业计算,物流管理. E-mail: ldn@bit.edu.cn

An Inter-cell Scheduling Approach Considering Transportation Capacity Constraints

Funds: 

Supported by National Natural Science Foundation of China (71401014), and Beijing Natural Science Foundation (4122069)

  • 摘要: 工件在生产单元之间频繁转移产生了跨单元调度问题.本文结合我国装备制造业的生产实际,提出考虑运输能力的跨单元调度方法,设计了一种基于离散蜂群与决策块结构的超启发式算法.针对传统超启发式算法的局限性提出动态决策块策略, 同时改进传统蜂群算法的侦查蜂策略,使之具有更好的优化性能.实验表明,动态决策块具有比静态决策块更好的性能,算法在优化能力和计算效率的综合性能上优势显著,并且问题的规模越大,优势越明显.
  • [1] Li D N, Wang Y. Production scheduling in intercell cooperative production mode. In: Proceedings of the 24th Chinese Control and Decision Conference (CCDC). Taiyuan: IEEE, 2012. 504-506
    [2] [2] Garza O, Smunt T L. Countering the negative impact of intercell flow in cellular manufacturing. Journal of Operations Management, 1991, 10(1): 92-118
    [3] [3] Johnson D J, Wemmerlov U. Why does cell implementation stop? factors influencing cell penetration in manufacturing plants. Production and Operations Management, 2004, 13(3): 272-289
    [4] [4] Li D N, Meng X W, Li M, Tian Y N. An ACO-based intercell scheduling approach for job shop cells with multiple single processing machines and one batch processing machine. Journal of Intelligent Manufacturing, DOI: 10.1007/s10845-013-0859-2
    [5] [5] Mosbah A B, Dao T M. Optimimization of group scheduling using simulation with the meta-heuristic extended great deluge (EGD) approach. In: Proceedings of the 2010 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). Macao: IEEE, 2010. 275-280
    [6] [6] Yousef G K, Reza T M, Amir K. Solving a multi-criteria group scheduling problem for a cellular manufacturing system by scatter search. Journal of the Chinese Institute of Industrial Engineers, 2011, 28(3): 192-205
    [7] [7] Solimanpur M, Elmi A. A tabu search approach for group scheduling in buffer-constrained flow shop cells. International Journal of Computer Integrated Manufacturing, 2011, 24(3): 257-268
    [8] [8] Tang J F, Wang X Q, Kaku I, Yung K L. Optimization of parts scheduling in multiple cells considering intercell move using scatter search approach. Journal of Intelligent Manufacturing, 2009, 21(4): 525-537
    [9] [9] Elmi A, Solimanpur M, Topaloglu S, Elmi A. A simulated annealing algorithm for the job shop cell scheduling problem with intercellular moves and reentrant parts. Computers Industrial Engineering. 2011, 61(1): 171-178
    [10] Li D N, Wang Y, Xiao G X, Tang J F. Dynamic parts scheduling in multiple job shop cells considering intercell moves and flexible routes. Computers Operations Research, 2013, 40(5): 1207-1223
    [11] Burke E K, Hyde M, Kendall G, Ochoa G, zcan E, Woodward J R. A classification of hyper-heuristic approaches. Handbook of Metaheuristics. Berlin: Springer, 2010. 449-468
    [12] Burke E K, Gendreau M, Hyde M, Kendall G, Ochoa G, zcan E, Qu R. Hyper-heuristics: a survey of the state of the art. Journal of the Operational Research Society, 2013, 64(12): 1695-1724
    [13] Yang T, Kuo Y, Cho C. A genetic algorithms simulation approach for the multi-attribute combinatorial dispatching decision problem. European Journal of Operational Research, 2007, 176(3): 1859-1873
    [14] Vzquez-Rodrguez J A, Petrovic S. A new dispatching rule based genetic algorithm for the multi-objective job shop problem. Journal of Heuristics, 2010, 16(6): 771-793
    [15] Fayad C, Petrovic S. A fuzzy genetic algorithm for real-world job shop scheduling. In: Proceedings of the 18th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems. Bari, Italy: IEA, 2005. 524-533
    [16] Li D N, Meng X W, Liang Q Q, Zhao J Q. A heuristic-search genetic algorithm for multi-stage hybrid flow shop scheduling with single processing machines and batch processing machines. Journal of Intelligent Manufacturing, DOI: 10.1007/s10845-014-0874-y
    [17] Karaboga D. An Idea Based on Honey Bee Swarm for Numerical Optimization, Technical Report-TR06, Computer Engineering Department, Engineering Faculty, Erciyes University, Turkey, 2005.
    [18] Pan Q K, Tasgetiren M F, Suganthan P N, Chua T J. A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem. Information Sciences, 2011, 181(12): 2455-2468
    [19] Tasgetiren M F, Pan Q K, Suganthan P N, Chen A H L. A discrete artificial bee colony algorithm for the total flowtime minimization in permutation flow shops. Information Sciences, 2011, 181(16): 3459-3475
  • 加载中
计量
  • 文章访问数:  1758
  • HTML全文浏览量:  104
  • PDF下载量:  943
  • 被引次数: 0
出版历程
  • 收稿日期:  2014-07-21
  • 修回日期:  2014-12-04
  • 刊出日期:  2015-05-20

目录

    /

    返回文章
    返回