无统计信息假设下的多阶段报童决策

doi:10.16381/j.cnki.issn1003-207x.2015.05.014

Abstract

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.

Key words: multi-period newsvendor problem, salvage value, no statistical assumption, weak aggregating algorithm, return loss function

CLC Number:

F224.10

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.

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

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