Optimal Inventory Policies when the Demand Distribution is not Known
November 1, 2000
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Summary
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 non-parametric 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 on 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.
Subject: Bayesian models, Stocks
Keywords: distribution function, WP
Pages:
24
Volume:
2000
DOI:
Issue:
183
Series:
Working Paper No. 2000/183
Stock No:
WPIEA1832000
ISBN:
9781451859300
ISSN:
1018-5941
