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Multi-period stochastic covering location problems: Modeling framework and solution approach

Author

Listed:
  • Marín, Alfredo
  • Martínez-Merino, Luisa I.
  • Rodríguez-Chía, Antonio M.
  • Saldanha-da-Gama, Francisco

Abstract

This paper introduces a very general discrete covering location model that accounts for uncertainty and time-dependent aspects. A MILP formulation is proposed for the problem. Afterwards, it is observed that most of the models existing in the literature related with covering location can be considered as particular cases of this formulation. In order to tackle large instances of this problem a Lagrangian relaxation based heuristic is developed. A computational study is addressed to check the potentials and limits of the formulation and some variants proposed for the problem, as well as to evaluate the heuristic. Finally, different measures to report the relevance of considering a multi-period stochastic setting are studied.

Suggested Citation

  • Marín, Alfredo & Martínez-Merino, Luisa I. & Rodríguez-Chía, Antonio M. & Saldanha-da-Gama, Francisco, 2018. "Multi-period stochastic covering location problems: Modeling framework and solution approach," European Journal of Operational Research, Elsevier, vol. 268(2), pages 432-449.
    • Handle: RePEc:eee:ejores:v:268:y:2018:i:2:p:432-449
    DOI: 10.1016/j.ejor.2018.01.040
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    References listed on IDEAS

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    Cited by:

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    2. Vatsa, Amit Kumar & Jayaswal, Sachin, 2021. "Capacitated multi-period maximal covering location problem with server uncertainty," European Journal of Operational Research, Elsevier, vol. 289(3), pages 1107-1126.
    3. Han, Jialin & Zhang, Jiaxiang & Zeng, Bing & Mao, Mingsong, 2021. "Optimizing dynamic facility location-allocation for agricultural machinery maintenance using Benders decomposition," Omega, Elsevier, vol. 105(C).
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    6. Liu, Kanglin & Liu, Changchun & Xiang, Xi & Tian, Zhili, 2023. "Testing facility location and dynamic capacity planning for pandemics with demand uncertainty," European Journal of Operational Research, Elsevier, vol. 304(1), pages 150-168.

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