Optimal Inventory Policies When the Demand Distribution is Not Known
AbstractThis 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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Bibliographic InfoPaper provided by International Monetary Fund in its series IMF Working Papers with number 00/183.
Date of creation: 01 Nov 2000
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Other versions of this item:
- Larson, C. Erik & Olson, Lars J. & Sharma, Sunil, 2001. "Optimal Inventory Policies when the Demand Distribution Is Not Known," Journal of Economic Theory, Elsevier, Elsevier, vol. 101(1), pages 281-300, November.
- Larson, C.E. & Olson, L.J. & Sharma, S., 1992. "Optimal Inventory Policies when the Demand Distribution is not Known," The A. Gary Anderson Graduate School of Management, The A. Gary Anderson Graduate School of Management. University of California Riverside 92-12, The A. Gary Anderson Graduate School of Management. University of California Riverside.
- C. Erik Larson Lars J. Olson** and Sunil Sharma***, 1991. "Optimal Inventory Policies When The Demand Distribution Is Not Known#," UCLA Economics Working Papers, UCLA Department of Economics 631, UCLA Department of Economics.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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