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The determination of optimal base‐stock inventory policy when the costs of under‐ and oversupply are uncertain

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  • Timothy J. Lowe
  • Leroy B. Schwarz
  • Edward J. McGavin

Abstract

This article considers the determination of the optimal base‐stock inventory policy for the newsboy inventory model when there is uncertainty about either or both of its basic cost inputs: either Cu, the marginal cost of an undersupply mistake, or Co, the marginal cost of an oversupply mistake. Such uncertainties often arise in implementing the newsboy model, especially with respect to Cu, whose value depends mostly on the often‐imponderable economic consequences of a lost sale or backorder. Given this uncertainty, we use decision theory to propose and analyze two measures of policy “goodness” and two base‐stock selection criteria, which in combination provide four alternative “optimal” base‐stock policies. Formulas and/or conditions defining each alternative policy are provided. Our empirical study indicates that the recommended policy can be quite sensitive to the measure/criterion chosen, and that the consequences of the wrong choice can be quite considerable.

Suggested Citation

  • Timothy J. Lowe & Leroy B. Schwarz & Edward J. McGavin, 1988. "The determination of optimal base‐stock inventory policy when the costs of under‐ and oversupply are uncertain," Naval Research Logistics (NRL), John Wiley & Sons, vol. 35(4), pages 539-554, August.
  • Handle: RePEc:wly:navres:v:35:y:1988:i:4:p:539-554
    DOI: 10.1002/1520-6750(198808)35:43.0.CO;2-2
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    References listed on IDEAS

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    1. Arthur F. Veinott, 1965. "The Optimal Inventory Policy for Batch Ordering," Operations Research, INFORMS, vol. 13(3), pages 424-432, June.
    2. Gary D. Eppen, 1979. "Note--Effects of Centralization on Expected Costs in a Multi-Location Newsboy Problem," Management Science, INFORMS, vol. 25(5), pages 498-501, May.
    3. B. Martos, 1965. "The Direct Power of Adjacent Vertex Programming Methods," Management Science, INFORMS, vol. 12(3), pages 241-252, November.
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