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Optimal Inventory Policies when the Demand Distribution is not Known

Author

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  • Larson, C.E.
  • Olson, L.J.
  • Sharma, S.

Abstract

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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Suggested Citation

  • 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.
    • Handle: RePEc:fth:caland:92-12
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    Cited by:

    1. Janssen, E. & Strijbosch, L.W.G. & Brekelmans, R.C.M., 2006. "Assessing the Effects of using Demand Parameters Estimates in Inventory Control," Other publications TiSEM e61834bf-8202-4a25-9311-f, Tilburg University, School of Economics and Management.
    2. Janssen, Elleke & Strijbosch, Leo & Brekelmans, Ruud, 2009. "Assessing the effects of using demand parameters estimates in inventory control and improving the performance using a correction function," International Journal of Production Economics, Elsevier, vol. 118(1), pages 34-42, March.
    3. Mor Armony & Erica L. Plambeck, 2005. "The Impact of Duplicate Orders on Demand Estimation and Capacity Investment," Management Science, INFORMS, vol. 51(10), pages 1505-1518, October.
    4. Sandun C. Perera & Suresh P. Sethi, 2023. "A survey of stochastic inventory models with fixed costs: Optimality of (s, S) and (s, S)‐type policies—Discrete‐time case," Production and Operations Management, Production and Operations Management Society, vol. 32(1), pages 131-153, January.

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