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 firmâs 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 UCLA Department of Economics in its series UCLA Economics Working Papers with number 631.
Date of creation: 01 Sep 1991
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Web page: http://www.econ.ucla.edu/
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, vol. 101(1), pages 281-300, November.
- Erik W. Larson & Sunil Sharma & Lars J. Olson, 2000. "Optimal Inventory Policies When the Demand Distribution is Not Known," IMF Working Papers 00/183, International Monetary Fund.
- 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 92-12, The A. Gary Anderson Graduate School of Management. University of California Riverside.
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