Optimal inventory policies when the demand distribution is not known

• 成果类型: Article
• 署名作者: Larson, CE; Olson, LJ; Sharma, S
• 署名单位: United States Department of the Treasury; Office of the Comptroller of the Currency; University System of Maryland; University of Maryland College Park; International Monetary Fund
• 刊物名称: JOURNAL OF ECONOMIC THEORY
• ISSN/ISSBN: 0022-0531
• DOI: 10.1006/jeth.2000.2772
• 发表日期: 2001
• 页码: 281-300
• 关键词: Inventory models; nonparametric Bayesian learning; Dirichlet process

摘要

This paper analyzes the stochastic inventory control problem when the demand distribution is not known, In contrast to previous Bayesian inventory models, this paper adopts a nonparametric Bayesian approach in which the firm's prior information is characterized by a Dirichlet process prior. This provides considerable freedom in the specification of prior information about demand, and it permits the accommodation of fixed order costs. As information oil the demand distribution accumulates, optimal history-dependent (s, S) rules are shown to converge to an (s, S) rule that is optimal when, the underlying demand distribution is known. (C) 2001 Academic Press.