能源回购补偿机制下基于无限阶段折扣准则的最优生产与库存策略

运筹与管理 ›› 2014, Vol. 23 ›› Issue (5): 109-119.

能源回购补偿机制下基于无限阶段折扣准则的最优生产与库存策略

张继红¹, 丁晓松^1,2, 陈虹桥²

1.北京外国语大学国际商学院,北京 100089;
2.中国科学院数学与系统科学研究院应用数学所,北京 100190

收稿日期:2013-03-21 出版日期:2021-05-25
通讯作者: 丁晓松(1977-),男,博士,教授,研究方向:物流与供应链管理,模糊决策分析;陈虹桥(1989-),男,博士生,研究方向:物流与供应链管理。
作者简介:张继红(1967-),男,博士,教授,研究方向:物流与供应链管理,马氏决策分析;
基金资助:
国家自然科学基金 (71371032,71071023);北京外国语大学规划项目(2011XG003);中央高校基本科研业务费(2012XJ030);北京高校青年英才计划(YETP0851)

Optimal Production-inventory Policy under an Energy Buy-back Program over an Infinite Planning Horizon

ZHANG Ji-hong¹, DING Xiao-song^1,2, CHEN Hong-qiao²

1. International Business School, Beijing Foreign Studies University, Beijing 100089, China;
2. Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China

Received:2013-03-21 Online:2021-05-25

摘要/Abstract

摘要： 世界经济的快速发展和工业化进程的推进促使各国电力需求激增,电力供需矛盾为能源回购项目的发展提供了条件。为能够实现错峰用电和缓解能源需求的紧张,能源回购项目在每个阶段出现能源短缺时,将根据短缺的不同程度为限产(或停产)企业提供了金额不同的资金补偿。因此,在该能源回购补偿机制下,企业需要确定每个阶段是否参加能源回购项目及其相应的生产库存策略,来实现其期望折扣成本的最小化。本文研究了能源回购补偿机制下企业以最小化期望折扣成本为目标的无限阶段最优生产/库存策略。引入启动成本和多个能源需求状态的资金补偿水平后,在合理的假设条件下,证明了每个阶段生产商的最优生产/库存策略在高峰状态为(sⁱ,S)策略,在非高峰状态为(s⁰,S,A)策略。

关键词: 管理科学与工程, 供应链管理, 生产/库存决策, 无限阶段折扣准则, 动态规划, 能源回购

Abstract: Due to the rising power demand with the rapid development of economics and enhancement of industrialization, the contradiction between supply and demand provides energy buy-back programs with great opportunities. Such programs, when activated, offer certain amount of financial compensations to participants for reducing their energy use, and aim to encourage participants to shift their electricity usage from peak to non-peak time. Hence, a manufacturer has to deal with the balance between receiving financial compensations from participation by reducing production and increasing sales from satisfying customers' demands through production/inventory. This paper studies a periodic review production/inventory control problem under an energy buy-back program over an infinite planning horizon. With the introduction of a fixed setup cost and compensation levels corresponding to different market states, we intend to identify the optimal production/inventory policy in order to minimize the discounted cost. Under mild assumptions, it is shown that such an optimal inventory policy is state dependent, and is of either an(sⁱ,S)or(s⁰,S,A)type by using Veinott's conditions.

Key words: management science and engineering, supply chain management, production/inventory decision, infinite planning horizon with discounting, dynamic programming, energy buy-back

中图分类号:

O227

张继红, 丁晓松, 陈虹桥. 能源回购补偿机制下基于无限阶段折扣准则的最优生产与库存策略[J]. 运筹与管理, 2014, 23(5): 109-119.

ZHANG Ji-hong, DING Xiao-song, CHEN Hong-qiao. Optimal Production-inventory Policy under an Energy Buy-back Program over an Infinite Planning Horizon[J]. Operations Research and Management Science, 2014, 23(5): 109-119.

参考文献

[1] Coy P. Exploiting uncertainty: the “real options” revolution in decision making[J]. Business Week Online June, 1999, 7: 118-124.
[2] Wald M. Utilities try new ways to vary energy pricing[J]. NY Times July,2000. 17.
[3] Chen F, Sethi S, Zhang H. A production-inventory problem for an energy buy-back program.[J]. IEEE Transactions on Automation Science and Engineering, 2007, 4(3): 395- 406.
[4] Chao X, Chen F. An optimal production and shutdown strategy when a supplier orders an incentive program[J]. Manufacturing Service & Operations Management,2005, 7: 130-143.
[5] 张继红,丁晓松,陈曦.能源回购补偿机制下的联合生产与定价策略[J].系统科学与数学,2011,31(10):1297-1305.
[6] Ding X, Zhang J, Chen X. A joint pricing and inventory control problem under an energy buy-back program[J]. Operations Research Letters, 2012, 40(6): 516-520.
[7] Iglehart D. Optimality of(s;S)policies in the infinite-horizon dynamic inventory problem[J]. Management Science, 1963, 9: 259-267.
[8] Beyer D, Cheng F, Sethi S, Taksar M. Markovian demand inventory models[M]. Springer, New York, 2010.
[9] Beyer D, Sethi S, Taksar M. Inventory models with markovian demands and cost functions of polynomial growth[J]. Journal of Optimization Theory and Applications, 1998, 98(2): 281-323.
[10] Sethi S, Cheng F. Optimality of(s;S)policies in inventory models with markovian demands[J]. Operations Research, 1997, 45, 931-939.
[11] Song J, Zipkin P. Inventory control in a fluctuating demand environment[J]. Operations Research, 1993, 41: 351-370.
[12] Veinott A. On the optimality of(s;S)policies: new conditions and a new proof[J]. SIAM Journal on Applied Mathematics, 1966, 14: 1067-1083.
[13] Beyer D, Sethi S. Average cost optimality in inventory models with markovian demands[J]. Journal of Optimization Theory and Applications, 1997, 92(3): 497-526.
[14] Chen X, Simchi Levi D. Coordinating inventory control and pricing strategies with random demand and fixed ordering cost: the infinite horizon case[J]. Mathematics of Operations Research, 2004, 29(3): 698-723.

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