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Integrated dynamic single-facility location and inventory planning problems

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  • Jiaming Qiu
  • Thomas Sharkey

Abstract

This article considers a class of dynamic single-article facility location problems in which the facility must determine order and inventory levels to meet the dynamic demands of the customers over a finite horizon. The motivating application of this class of problems is in military logistics and the decision makers in this area are not only concerned with the logistical costs of the facility but also with centering the facility among the customers in each time period in order to be able to provide other services. Both the location plan and inventory plan of the facility in the problem must be determined while considering these different metrics associated with the performance of these plans. Effective dynamic programming algorithms for this class of problem are provided for both of these metrics. These dynamic programming algorithms are utilized in order to construct the efficient frontier associated with these two metrics in polynomial time. Computational testing indicates that these algorithms can be used in planning activities for military logistics. [Supplemental materials are available for this article. Go to the publisher’s online edition of IIE Transactions for a worst-case example of constructing the efficient frontier.]

Suggested Citation

  • Jiaming Qiu & Thomas Sharkey, 2013. "Integrated dynamic single-facility location and inventory planning problems," IISE Transactions, Taylor & Francis Journals, vol. 45(8), pages 883-895.
  • Handle: RePEc:taf:uiiexx:v:45:y:2013:i:8:p:883-895
    DOI: 10.1080/0740817X.2013.770184
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    Citations

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    Cited by:

    1. Mofidi, Seyed Shahab & Pazour, Jennifer A. & Roy, Debjit, 2018. "Proactive vs. reactive order-fulfillment resource allocation for sea-based logistics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 66-84.
    2. Junxuan Li & Chelsea C. White, 2023. "Capacity planning in a decentralized autologous cell therapy manufacturing network for low-cost resilience," Flexible Services and Manufacturing Journal, Springer, vol. 35(2), pages 295-319, June.
    3. Lei, Chao & Lin, Wei-Hua & Miao, Lixin, 2014. "A multicut L-shaped based algorithm to solve a stochastic programming model for the mobile facility routing and scheduling problem," European Journal of Operational Research, Elsevier, vol. 238(3), pages 699-710.
    4. Garrett, Richard A. & Sharkey, Thomas C. & Grabowski, Martha & Wallace, William A., 2017. "Dynamic resource allocation to support oil spill response planning for energy exploration in the Arctic," European Journal of Operational Research, Elsevier, vol. 257(1), pages 272-286.
    5. Satya S. Malladi & Alan L. Erera & Chelsea C. White, 2021. "Managing mobile production-inventory systems influenced by a modulation process," Annals of Operations Research, Springer, vol. 304(1), pages 299-330, September.
    6. Yuli Zhang & Amber R. Richter & Jeyaveerasingam George Shanthikumar & Zuo‐Jun Max Shen, 2022. "Dynamic Inventory Relocation in Disaster Relief," Production and Operations Management, Production and Operations Management Society, vol. 31(3), pages 1052-1070, March.

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