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Stochastic Dynamic Location Analysis

Author

Listed:
  • Richard E. Rosenthal

    (University of Tennessee)

  • John A. White

    (Georgia Institute of Technology)

  • Donovan Young

    (Georgia Institute of Technology)

Abstract

This research introduces methods of stochastic decision processes into location analysis. The specific model concerns making dynamic relocation decisions for a new facility (server) that must interact with existing facilities (customers) whose relocations are stochastic processes. The model is an infinite-horizon Markov decision chain whose solution gives a server relocation policy that minimizes the expected discounted sum of costs. Costs are location-dependent and are incurred in two ways: when the server makes choice relocations and when the server interacts with customers. The model captures the essence of a variety of familiar dynamic location decision situations. Some methodological developments that allow solution of large problems are reported.

Suggested Citation

  • Richard E. Rosenthal & John A. White & Donovan Young, 1978. "Stochastic Dynamic Location Analysis," Management Science, INFORMS, vol. 24(6), pages 645-653, February.
    • Handle: RePEc:inm:ormnsc:v:24:y:1978:i:6:p:645-653
    DOI: 10.1287/mnsc.24.6.645
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    Cited by:

    1. Dimitri P. Bertsekas & Huizhen Yu, 2012. "Q-Learning and Enhanced Policy Iteration in Discounted Dynamic Programming," Mathematics of Operations Research, INFORMS, vol. 37(1), pages 66-94, February.
    2. Badri, Masood A., 1999. "Combining the analytic hierarchy process and goal programming for global facility location-allocation problem," International Journal of Production Economics, Elsevier, vol. 62(3), pages 237-248, September.
    3. 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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