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基于马尔科夫决策过程的ATO系统独立组件与产品双需求最优决策研究

李稚 谭德庆

李稚, 谭德庆. 基于马尔科夫决策过程的ATO系统独立组件与产品双需求最优决策研究. 自动化学报, 2016, 42(5): 782-791. doi: 10.16383/j.ass.2016.c150488
引用本文: 李稚, 谭德庆. 基于马尔科夫决策过程的ATO系统独立组件与产品双需求最优决策研究. 自动化学报, 2016, 42(5): 782-791. doi: 10.16383/j.ass.2016.c150488
LI Zhi, TAN De-Qing. Optimal Control of ATO System with Individual Components and Product Demands Based on Markov Decision Process. ACTA AUTOMATICA SINICA, 2016, 42(5): 782-791. doi: 10.16383/j.ass.2016.c150488
Citation: LI Zhi, TAN De-Qing. Optimal Control of ATO System with Individual Components and Product Demands Based on Markov Decision Process. ACTA AUTOMATICA SINICA, 2016, 42(5): 782-791. doi: 10.16383/j.ass.2016.c150488

基于马尔科夫决策过程的ATO系统独立组件与产品双需求最优决策研究

doi: 10.16383/j.ass.2016.c150488
基金项目: 

教育部第49批留学回国人员科研启动基金 [2015]311

详细信息
    作者简介:

    谭德庆 西南交通大学经济管理学院教授,博士.主要研究方向为博弈理论与应用,决策科学.E-mail:tandeqing@home.swjtu.edu.cn

    通讯作者:

    李稚 天津工业大学管理学院讲师,博士.2013年获得法国里尔中央理工大学自动化信息处理专业博士学位.主要研究方向为ATO系统生产与库存的优化控制,决策科学.本文通信作者.E-mail:lizhi@tjpu.edu.cn

Optimal Control of ATO System with Individual Components and Product Demands Based on Markov Decision Process

Funds: 

the Project Sponsored by the Scientific Research Foundation for the Returned Overseas Chinese Scholars (No.49 Program) , State Education Ministry Science Foundation of China [2015]311

More Information
    Author Bio:

    Ph.D., professor at the School of Economics Management & Southwest Jiaotong University. His research interest covers the application of game theory and decision science

    Corresponding author: LI Zhi Ph.D., lecturer at the School of Management, Tianjin Polytechnic University. She received her Ph.D. degree from Ecole Centrale de Lille, France in 2013. Her research interest covers optimal control of the production and inventory in ATO system and decision science. Corresponding author of this paper
  • 摘要: 研究多维组件, 单一产品的双需求型面向订单装配(Assemble-to-order, ATO)系统. 产品需求为延期交货型, 当其不被满足时将产生缺货等待成本; 而独立组件需求为销售损失型, 其不被满足时将产生缺货损失成本. 该问题可以抽象成一个动态马尔科夫决策过程(Markov decision process, MDP), 通过对双需求模型求解得到状态依赖型最优策略, 即任一组件的最优生产--库存策略由系统内其他组件的库存水平决定. 研究解决了多需求复杂ATO系统的生产和库存优化控制问题. 提出在一定条件下, 组件的基础库存值可以等价于最终产品需求的库存配给值. 组件的基础库存值与库存配给值随系统内其他组件库存的增加而增加, 而产品需求的库存配给值随系统组件库存和产品缺货量的增加而减少. 最后通过数值实验分析缺货量及组件库存对最优策略结构的影响, 并得到了相应的企业生产实践的管理启示.
  • 图  1  ATO系统组件最优生产策略

    Fig.  1  Optimal production policy for component in ATO

    图  2  ATO系统产品需求的最优库存配给策略

    Fig.  2  Optimal inventory allocation policy for product demand in ATO

    图  3  ATO系统独立组件需求最优库存配给策略

    Fig.  3  Optimal inventory allocation policy for individual component demand in ATO

    表  1  最优策略vs.启发式算法策略

    Table  1  Optimal policy versus heuristics

    $\lambda_0$ $\lambda_1$ $ \lambda_2$ $\mu_1$ $\mu_2$ $h_1$ $h_2$ $b_0$ $c_1$ $c_2$ $\frac{({v^{H}-v^{\ast}})}{v^{\ast}}(\%)$
    1 1.05 0.43 1.71 2.43 3.06 5.90 4.63 82.17 117.63 276.33 1.574
    2 0.601.032.452.983.425.594.9669.88227.82454.585.734
    3 1.322.601.073.843.224.824.6047.74182.11199.481.371
    4 1.702.462.374.953.975.766.7492.02310.57405.302.429
    5 0.265.191.664.553.363.514.4786.85483.11384.783.099
    6 0.791.811.984.573.182.025.4966.18209.95332.790.686
    7 0.691.651.962.634.956.846.6952.93427.26386.806.115
    8 0.263.792.593.463.202.168.5869.10480.45197.100.243
    9 1.971.040.424.814.011.695.6654.73200.01164.375.052
    10 1.592.791.453.803.976.806.5583.89277.30173.661.627
    11 0.461.841.632.671.928.077.2749.97352.29277.871.169
    12 0.781.883.194.203.991.497.4693.58348.68465.150.185
    13 1.011.071.942.573.803.843.5751.64297.04144.262.873
    14 0.300.241.310.722.689.429.1540.28142.12356.000.604
    15 1.142.470.694.693.544.657.08108.09388.54305.734.842
    16 1.420.776.512.449.828.563.6674.49318.02219.481.798
    17 0.044.732.185.822.161.479.61210.47458.20143.432.829
    18 1.301.387.434.359.464.922.30178.88155.86188.075.380
    19 2.161.914.314.967.815.035.8393.49365.78443.311.373
    20 1.007.478.129.578.105.471.47155.48220.94353.924.670
    21 2.674.582.888.308.026.855.54135.60198.65492.381.830
    22 2.367.222.059.954.977.442.7283.86100.10296.271.096
    23 0.347.997.547.647.752.137.06206.66421.96331.711.182
    24 1.232.419.736.799.502.213.22204.06369.70195.206.379
    25 1.202.773.615.977.519.054.40213.99474.80100.032.233
    26 1.566.876.549.028.318.586.56166.86227.97371.9911.512
    27 0.548.362.549.545.381.261.68119.78305.63255.310.126
    28 1.504.379.176.439.952.051.90127.34393.21390.502.209
    29 1.716.672.807.168.619.973.28103.01277.92410.011.778
    30 1.09 9.29 6.05 9.81 7.01 3.85 9.17 160.18 493.08 439.47 1.207
    (注: $\lambda_0 \sim {\rm U}(0,10)$, $\lambda_k \sim {\rm U}(0,10)$, $\mu_k \sim {\rm U}(1,10)$, $0.5 \le \rho _k \le 1.2$, $h_k \sim {\rm U}(1,10)$, $b_0 \sim {\rm U}(5,15) \times \sum\nolimits_{k = 1}^2 {h_k }$, $c_k \sim {\rm U}(100,500)$, }\\ \multicolumn{12}{l}{$\rho _k =\left( {\lambda _0 + \lambda _k } \right) / {\mu _k }$, $k = 1,2$.)
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  • [1] Song J S, Zipkin P. Supply chain operations: assemble-to-order systems, Chapter 11 in handbooks in operations research and management science. Supply Chain Management, 2003, 11(1): 561-596
    [2] De Véricourt F, Karaesmen F, Dallery Y. Optimal stock allocation for a capacitated supply system. Management Science, 2002, 48(11): 1486-1501
    [3] Karaarslan A G, Kiesmüller G P, De Kok A G. Analysis of an assemble-to-order system with different review periods. International Journal of Production Economics, 2013, 143(2): 335-341
    [4] Saidane S, Babai M Z, Aguir M S, Korbaa O. On the performance of the base-stock inventory system under a compound erlang demand distribution. Computers & Industrial Engineering, 2013, 66(3): 548-554
    [5] Juan A A, Grasman S E, Cáceres-Cruz J, Bektas T. A simheuristic algorithm for the single-period stochastic inventory-routing problem with stock-outs. Simulation Modelling Practice and Theory, 2014, 46: 40-52
    [6] Benjaafar S, ElHafsi M. Production and inventory control of a single product assemble-to-order system with multiple customer classes. Management Science, 2006, 52(12): 1896-1912
    [7] ElHafsi M, Li Z, Camus H, Craye E. An assemble-to-order system with product and components demand with lost sales. International Journal of Production Research, 2015, 53(3): 718-735
    [8] 娄山佐, 田新诚. 随机供应中断和退货环境下库存问题的建模与控制. 自动化学报, 2014, 40(11): 2436-2443

    Lou Shan-Zuo, Tian Xin-Cheng. Modeling and control for inventory with stochastic supply disruptions and returns. Acta Automatica Sinica, 2014, 40(11): 2436-2443
    [9] 娄山佐, 田新诚. 随机供应中断和退货环境下库存的应急控制. 自动化学报, 2015, 41(1): 94-103

    Lou Shan-Zuo, Tian Xin-Cheng. Contingent control of inventory under stochastic supply disruptions and returns. Acta Automatica Sinica, 2015, 41(1): 94-103
    [10] 郭佳, 傅科, 陈功玉. 可变产能的按订单装配系统库存和生产决策研究. 中国管理科学, 2012, 20(3): 94-103

    Guo Jia, Fu Ke, Chen Gong-Yu. Optimal inventory and production decisions for an ATO system with variable capacity. Chinese Journal of Management Science, 2012, 20(3): 94-103
    [11] 刘艳梅, 任佳, 江支柱, 刘曦泽, 祁国宁. 大批量定制下按订单装配产品同步生产计划方法. 计算机集成制造系统, 2014, 20(6): 1352-1358

    Liu Yan-Mei, Ren-Jia, Jiang Zhi-Zhu, Liu Xi-Ze, Qi Guo-Ning. Synchronized production planning method for assemble to order products of mass customization. Computer Integrated Manufacturing Systems, 2014, 20(6): 1352-1358
    [12] Kim B, Kim J. A single server queue with Markov modulated service rates and impatient customers. Performance Evaluation, 2015, 83-84: 1-15
    [13] Puterman M L. Markov Decision Processes: Discrete Stochastic Dynamic Programming. New York: John Wiley and Sons, 1994. 158-164
    [14] Lippman S A. Applying a new device in the optimization of exponential queuing systems. Operations Research, 1975, 23(4): 687-710
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  • 收稿日期:  2015-07-28
  • 录用日期:  2016-01-27
  • 刊出日期:  2016-05-01

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