考虑装卸的柔性作业车间双资源调度问题

吴秀丽1 肖晓1 赵宁1

(1.北京科技大学机械工程学院, 北京 100083)

【摘要】针对“研产混线”中各类制造资源利用率低、非加工时间过长、调度难度大的问题,以生产中最紧缺的夹具资源为例,提出考虑装卸的柔性作业车间双资源调度问题.首先,以最小化完工时间和准结时间为目标建立该问题的数学优化模型;然后,设计快速非支配排序遗传算法对问题进行求解,根据问题特性综合考虑两个目标并设计降准解码算法,随机从交叉算子池和变异算子池中选择算子进行操作,根据非支配等级和拥挤度选择进入下一代的个体;最后,通过数值实验表明,针对考虑装卸的柔性作业车间双资源调度问题,所提出算法能够有效求解该问题,保证完工时间的同时降低准结时间.

【关键词】 研产混线; 柔性作业车间调度问题; 双资源调度; 夹具; 准结时间;

【DOI】

【基金资助】 国家自然科学基金项目(51305024) 国防基础科研计划项目(JCKY2018209C002)

Download this article

    References

    [1]Tian C, Li L. Research on management and batch production control of aviation enterprises based on bottleneck management[C]. Proceedings of the Academic Exchange Meeting of the Management Science Branch of China Aviation Society in 2012. Beijing:Chinese Society of Aeronautics and Astronautics, 2012:717-726.

    [2]Li Z F. Research on job shop scheduling problems with multi-time and its engineering application[D].Wuhan:School of Mechanical Science and Engineering,Huazhong University of Science and Technology, 2010.

    [3] Allahverdi A. The third comprehensive survey on scheduling problems with setup times/costs[J]. European Journal of Operational Research, 2015, 246(2):345-378.

    [4] Heger J, Jürgen B, Hildebrandt T, et al. Dynamic adjustment of dispatching rule parameters in flow shops with sequence-dependent set-up times[J]. International Journal of Production Research, 2016, 54(22):6812-6824.

    [5] Benkalai I, Rebaine D, GagnéC, et al. Improving the migrating birds optimization metaheuristic for the permutation flow shop with sequence-dependent set-up times[J]. International Journal of Production Research,2017, 55(20):6145-6157.

    [6] Nourali S, Imanipour N. A particle swarm optimization-based algorithm for flexible assembly job shop scheduling problem with sequence dependent setup times[J]. Scientia Iranica, 2014, 21(3):1021-1033.

    [7] Sahin M, Kellegöz T. Increasing production rate in U-type assembly lines with sequence-dependent set-up times[J].Engineering Optimization, 2016, 49(8):1401-1419.

    [8] Lee K, Lei L, Pinedo M. Production scheduling with history-dependent setup times[J]. Naval Research Logistics, 2012, 59(1):58-68.

    [9] Aydilek A, Aydilek H, Allahverdi A. Minimising maximum tardiness in assembly flowshops with setup times[J]. International Journal of Production Research,2017, 55(24):7541-7565.

    [10]Tao S, Hu Z H, Sheng Z H. Time-dependent coordination strategies for key resource prioritized three-stage handling operations[J]. Control and Decision, 2015, 30(8):1441-1446.

    [11] Aldowaisan T, Allahverdi A. Total tardiness performance in M-machine no-wait flowshops with separate setup times[J]. Intelligent Control and Automation, 2015(6):38-44.

    [12]Luo Z Q, Wang R X, Lei J, et al. A Study on the assistant mechanical time quota[J], Machine Tool&Hydraulics,2004, 32(12):62-64.

    [13]Wu X L, Zhang Z Q, LI J Q. A brain storm optimization algorithm integrating diversity and discussion mechanism for solving discrete production scheduling problem[J].Control and Decision, 2017, 32(9):1583-1590.

    [14]Li J Y, Huang Y, Wang J Q, et al. Branch population genetic algorithm for extension dual resource constrained job shop scheduling problem[J]. Journal of Northwestern Polytechnical University, 2016, 34(4):635-641.

    [15]Guan Y Q, Zhu Y, Xie N M. Dynamic allocation model of manufacturing resources in flexible job shop considering multi-cost constraints[J]. Control and Decision, 2018,33(11):2037-2044.

    [16]Li J Y, Sun S D, Huang Y, et al. Double-objective inherited genetic algorithm for dual resource constrained job shop[J]. Control and Decision, 2011, 26(12):1761-1767.

    [17]Xu X L, Ying S Y, Wang W L. Fuzzy flexible job-shop scheduling method based on multi-agent immune algorithm[J]. Control and Decision, 2010, 25(2):171-178.

    [18] Deb K, Agrawal S, Pratap A, et al. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization:NSGA-II[C]. Proceedings for the International Conference on Parallel Problem Solving From Nature. Berlin, Heidelberg:Springer, 2000.

    [19] Wu X L, Sun Y J. A green scheduling algorithm for flexible job shop with energy-saving measures[J]. Journal of Cleaner Production, 2018, 172:3249-3264.

    [20] Akay B, Yao X. Automated scheduling and planning[M].Berlin:Springer, 2013:191-224.

    [21]Zhou Y W. Research of multi-objective flow shop scheduling based on fast non-dominated sorting genetic algorithm[D]. Guangzhou:School of Mathematics,South China University of Technology, 2015.

    [22]Wu X L, Cui Q. Multi-objective flexible flow shop scheduling problem with renewable energy[J].Computer Integrated Manufacturing Systems, 2018,24(11):2792-2807.

    [23] Brandimarte P. Routing and scheduling in a flexible job shop by tabu search[J]. Annals of Operations Research,1993, 41(3):157-183.

    [24]Ding Q F, Yin X Y. Research survey of differential evolution algorithms[J]. CAAI Transactions on Intelligent Systems, 2017, 12(4):431-442.

    [25]Shi Q. Research on the multi-objective optimization problem based on differential evolution algorithm[D].Shanghai:School of Information Science and Technology, Donghua University, 2016.

    [26]Han Y Y, Li J Q, Sang H Y, et al. Discrete NSGAfor multi-objective lot-streaming flow shop scheduling problem with limited buffers[J]. Journal of Liaocheng University:Natural Science Edition, 2018, 31(1):89-96.

    [27]He L J, Li W F, Zhang Y. Multi-objective optimization method based on grey synthetic incidence analysis[J].Control and Decision, 2020, 35(5):1134-1142.

    [28] Zitzler E, Thiele L. Multi-objective evolutionary algorithms:A comparative case study and the strength pareto approach[J]. IEEE Transactions on Evolutionary Computation, 1999, 3(4):257-271.

    [29] Gong M, Jiao L, Du H, et al. Multi-objective immune algorithm with nondominated neighbor-based selection[J]. Evolutionary Computation, 2008, 16(2):225-255.

This Article

ISSN:1001-0920

CN: 21-1124/TP

Vol 35, No. 10, Pages 2475-2485

October 2020

Downloads:1

Share
Article Outline

摘要

  • 0引言
  • 1 FJSDRSP-LU优化模型
  • 2 基于降准解码的NSGA-II改进算法
  • 3 数值实验
  • 4 结论
  • 参考文献