Optimal Inventory Policies when the Demand Distribution is not Known
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.
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|Date of creation:||1992|
|Date of revision:|
|Contact details of provider:|| Postal: The A. Gary Anderson Graduate School of Management. University of California, Riverside. Riverside CA 92521|
Web page: http://www.agsm.ucr.edu/
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"Optimal Search with Learning,"
UCLA Economics Working Papers
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