主管:中国科学院
主办:中国优选法统筹法与经济数学研究会
   中国科学院科技战略咨询研究院
Articles

Decision-making for Multi-period Newsvendor Problem Without Statistical Information Assumption

Expand
  • 1. School of Management, Guangdong University of Technology, Guangzhou 510520, China;
    2. School of Business Administration, South China University of Technology, Guangzhou 510640, China

Received date: 2013-05-01

  Revised date: 2014-02-07

  Online published: 2015-05-20

Abstract

The Weak Aggregating Algorithm (WAA) of prediction with expert advices, which advanced in computer science, is applied to study the multi-period newsvendor problem without making statistical assumption. WAA is an exponentially weighted average algorithm that updates the expert advice's weight according to loss function with initial weights distribution. Based on the return loss function and the expert advice of fixed stock level strategy, the decision-making method is used in this paper, which is in accord with the conclusions obtained using return function; and the case with salvage value is extended. Theoretically, it is proved that the cumulative loss the proposed decision-making method achieved does exceed that of the best expert advice. Numerical examples are presented to further illustrate the feasibility and rationality of the proposed decision-making method and explore the effect of selling and cost price on competitive performance;the results show that the introduction of salvage value greatly improves the competitive performance of the proposed decision-making method and thus presents important practical significance.

Cite this article

ZHANG Yong, ZHANG Wei-guo, XU Wei-jun . Decision-making for Multi-period Newsvendor Problem Without Statistical Information Assumption[J]. Chinese Journal of Management Science, 2015 , 23(5) : 107 -115 . DOI: 10.16381/j.cnki.issn1003-207x.2015.05.014

References

[1] Petruzzi N C, Dada M. Pricing and newsvendor problem: A review with extension[J]. Operations Research, 1999, 47( 2) : 183-149.

[2] Silver E A, Pyke D F, Peterson R P. Inventory management and production planning and scheduling[M]. New York: John Wiley, 1998.

[3] Khouja M. The single-period (news-vendor) problem: Literature review and suggestions for future research[J]. Omega, 1999, 27(5): 537-553.

[4] 汪小京, 刘志学, 郑长征. 多类顾客环境下报童模型中库存分配策略研究[J]. 中国管理科学, 2010, 18(4): 65-72.

[5] 黄松, 杨超, 张曦. 考虑战略顾客行为带预算约束的多产品报童问题[J]. 中国管理科学, 2011, 19(3): 70-78.

[6] 许民利, 李展. 基于CVaR准则具有预算约束和损失约束的报童决策[J]. 控制与决策, 2013, 28(11): 1614-1622.

[7] 周艳菊,应仁仁,陈晓红,等. 基于前景理论的两产品报童的订货模型[J]. 管理科学学报, 2013, 16(11): 17-29.

[8] Scarf H. Some remarks on Bayes solutions to the inventory problem[J]. Naval Research Logistics Quarterly, 1960, 7(4): 591-596.

[9] Scarf H. Bayes solution to the statistical inventory problem[J]. Annals of Mathematical Statistics, 1959, 30(2): 490-508.

[10] Karlin S. Dynamic inventory policy with varying stochastic demands[J]. Management Science, 1960, 6(3): 231-258.

[11] Iglehart D L. The dynamic inventory problem with unknown demand distribution[J]. Management Science, 1964, 10(3): 429-440.

[12] Liyanage L H, Shanthikumar J G. A practical inventory control policy using operational statistics[J]. Operations Research Letters, 2005, 33(4): 341-348.

[13] O'Neil S, Chaudhary A. Comparing online learning algorithms to stochastic approaches for the multi-period newsvendor problem[C]. Proceedings of the Tenth Workshop on Algorithm Engineering and Experiments, San Francisco,California,January19,2008.

[14] Huh W T, Janakiraman G, Muckstadt J A, et al. An adaptive algorithm for finding the optimal base-stock policy in lost sales inventory systems with censored demand[J]. Mathematics of Operations Research, 2009, 34 (2): 397-416.

[15] Huh W T, Levi R, Rusmevichientong P, et al. Adaptive data-driven inventory control policies based on Kaplan-Meier estimator for censored demand[J]. Operations Research, 2011, 59(4): 929-941.

[16] Huh W T, Rusmevichientong P. A non-parametric asymptotic analysis of inventory planning with censored demand[J]. Mathematics of Operations Research, 2009, 34 (1): 103-123.

[17] Zhu Zhisu, Zhang Jiawei, Ye Yinyu. Newsvendor optimization with limited distribution information[J]. Optimization Methods and Software, 2013, 28(3): 640-667.

[18] Kwon K, Cheong T. A minimax distribution-free procedure for a newsvendor problem with free shipping[J]. European Journal of Operational Research, 2014, 232(1): 234-240.

[19] Sleator D, Tarjan R. Amortized efficiency of list update and paging rules[J]. Communications of the ACM, 1985, 28(2): 202-208.

[20] Karlin A R, Manasse M S, Rudolph L, et al. Competitive snoopy caching[J]. Algorithmica, 1988, 3(1): 79-119.

[21] Borodin A, El-Yaniv R. Online computation and competitive analysis[M]. Cambridge:Cambridge University Press, 1998.

[22] Wagner M R. Fully distribution-free profit maximization: the inventory management case[J]. Mathematics of Operations Research, 2010, 35 (4): 728-741.

[23] Wagner M R. Online lot-sizing problems with ordering, holding and shortage costs[J]. Operations Research Letters, 2011, 39(2): 144-149.

[24] 张桂清,徐寅峰. 报童问题的最优竞争比策略及其风险补偿模型[J]. 管理学报, 2011, 8(1): 97-102.

[25] 张桂清,徐寅峰. 概率预期下在线报童问题的最小风险策略[J]. 中国管理科学, 2010, 18(6): 131-137.

[26] Ball M, Queyranne M. Toward robust revenue management: Competitive analysis of online booking[J]. Operations Research, 2009, 57 (4): 950-963.

[27] Van den Heuvel W, Wagelmans A P M. Worst case analysis for a general class of on-line lot-sizing heuristics[J]. Operations Research, 2010, 58 (1): 59-67.

[28] Cesa-Bianchi N, Lugosi G. Prediction, learning, and games[M]. Cambridge:Cambridge University Press, 2006.

[29] Vovk V. Competitive on-line statistics[J]. International Statistical Review, 2001, 69(2): 213-248.

[30] Kalnishkan Y, Vyugin M V. The weak aggregating algorithm and weak mixability[J]. The Journal of Computer and System Sciences, 2008, 74(8): 1228-1244.

[31] Levina T, Levin Y, McGill J, et al. Weak aggregating algorithm for the distribution-free perishable inventory problem[J]. Operations Research Letters, 2010, 38(6): 516-521.
Outlines

/