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Formulations and Approximation Algorithms for Multilevel Uncapacitated Facility Location

Author

Listed:
  • Camilo Ortiz-Astorquiza

    (Concordia University and Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), Montreal, Canada H3G 1M8)

  • Ivan Contreras

    (Concordia University and Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), Montreal, Canada H3G 1M8)

  • Gilbert Laporte

    (HEC Montréal and Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), Montreal, Canada H3T 2A7)

Abstract

This paper studies multilevel uncapacitated p -location problems, a general class of facility location problems. We use a combinatorial representation of the general problem where the objective function satisfies the submodular property, and we exploit this characterization to derive worst-case bounds for a greedy heuristic. We also obtain sharper bounds when the setup cost for opening facilities is zero and the allocation profits are nonnegative. Moreover, we introduce a mixed integer linear programming formulation for the problem based on the submodularity property. We present results of computational experiments to assess the performance of the greedy heuristic and that of the formulation. We compare the models with previously studied formulations.

Suggested Citation

  • Camilo Ortiz-Astorquiza & Ivan Contreras & Gilbert Laporte, 2017. "Formulations and Approximation Algorithms for Multilevel Uncapacitated Facility Location," INFORMS Journal on Computing, INFORMS, vol. 29(4), pages 767-779, November.
  • Handle: RePEc:inm:orijoc:v:29:y:2017:i:4:p:767-779
    DOI: 10.1287/ijoc.2017.0757
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    References listed on IDEAS

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    1. Ortiz-Astorquiza, Camilo & Contreras, Ivan & Laporte, Gilbert, 2015. "Multi-level facility location as the maximization of a submodular set function," European Journal of Operational Research, Elsevier, vol. 247(3), pages 1013-1016.
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    2. Dam, Tien Thanh & Ta, Thuy Anh & Mai, Tien, 2022. "Submodularity and local search approaches for maximum capture problems under generalized extreme value models," European Journal of Operational Research, Elsevier, vol. 300(3), pages 953-965.

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