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Economic ordering decisions with market choice flexibility

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  • Joseph Geunes
  • Zuo‐Jun Shen
  • H. Edwin Romeijn

Abstract

Standard approaches to classical inventory control problems treat satisfying a predefined demand level as a constraint. In many practical contexts, however, total demand is comprised of separate demands from different markets or customers. It is not always clear that constraining a producer to satisfy all markets is an optimal approach. Since the inventory‐related cost of an item depends on total demand volume, no clear method exists for determining a market's profitability a priori, based simply on per unit revenue and cost. Moreover, capacity constraints often limit a producer's ability to meet all demands. This paper presents models to address economic ordering decisions when a producer can choose whether to satisfy multiple markets. These models result in a set of nonlinear binary integer programming problems that, in the uncapacitated case, lend themselves to efficient solution due to their special structure. The capacitated versions can be cast as nonlinear knapsack problems, for which we propose a heuristic solution approach that is asymptotically optimal in the number of markets. The models generalize the classical EOQ and EPQ problems and lead to interesting optimization problems with intuitively appealing solution properties and interesting implications for inventory and pricing management. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2004.

Suggested Citation

  • Joseph Geunes & Zuo‐Jun Shen & H. Edwin Romeijn, 2004. "Economic ordering decisions with market choice flexibility," Naval Research Logistics (NRL), John Wiley & Sons, vol. 51(1), pages 117-136, February.
  • Handle: RePEc:wly:navres:v:51:y:2004:i:1:p:117-136
    DOI: 10.1002/nav.10109
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    References listed on IDEAS

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    1. William L. Maxwell, 1964. "The scheduling of economic lot sizes," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 11(2), pages 89-124, June.
    2. Gregory Dobson, 1988. "Sensitivity of the EOQ Model to Parameter Estimates," Operations Research, INFORMS, vol. 36(4), pages 570-574, August.
    3. Meir J. Rosenblatt & Yale T. Herer & Ilan Hefter, 1998. "Note. An Acquisition Policy for a Single Item Multi-Supplier System," Management Science, INFORMS, vol. 44(11-Part-2), pages 96-100, November.
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    Cited by:

    1. Wenjun Ni & Jia Shu & Miao Song & Dachuan Xu & Kaike Zhang, 2021. "A Branch-and-Price Algorithm for Facility Location with General Facility Cost Functions," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 86-104, January.
    2. Jia Shu & Chung-Piaw Teo & Zuo-Jun Max Shen, 2005. "Stochastic Transportation-Inventory Network Design Problem," Operations Research, INFORMS, vol. 53(1), pages 48-60, February.
    3. Wei Huang & H. Edwin Romeijn & Joseph Geunes, 2005. "The continuous‐time single‐sourcing problem with capacity expansion opportunities," Naval Research Logistics (NRL), John Wiley & Sons, vol. 52(3), pages 193-211, April.
    4. Dinçer Konur & Joseph Geunes, 2019. "Integrated districting, fleet composition, and inventory planning for a multi-retailer distribution system," Annals of Operations Research, Springer, vol. 273(1), pages 527-559, February.
    5. Shuang Chen & Joseph Geunes, 2013. "Optimal allocation of stock levels and stochastic customer demands to a capacitated resource," Annals of Operations Research, Springer, vol. 203(1), pages 33-54, March.
    6. Wilco van den Heuvel & Semra Ağralı & Z. Caner Taşkın, 2023. "A Decomposition Algorithm for Single and Multiobjective Integrated Market Selection and Production Planning," INFORMS Journal on Computing, INFORMS, vol. 35(6), pages 1439-1453, November.

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