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
580, UCLA Department of Economics.
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- Samuel Karlin, 1960. "Dynamic Inventory Policy with Varying Stochastic Demands," Management Science, INFORMS, vol. 6(3), pages 231-258, April.
- Michael Rothschild, 1974. "Searching for the Lowest Price When the Distribution of Prices Is Unknown: A Summary," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 3, number 1, pages 293-294 National Bureau of Economic Research, Inc.
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