The Stochastic Multiperiod Location Transportation Problem

  • Walid Klibi

    Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), Université de Montréal, Montréal, Quebéc H3C 3J7, Canada, and Faculté des Sciences de l'Administration, Département Opérations et Systémes de Décision, Université Laval, Québec City, Québec G1V 0A6, Canada

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  • Francis Lasalle

    Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), Université de Montréal, Montréal, Quebéc H3C 3J7, Canada, and Faculté des Sciences de l'Administration, Département Opérations et Systémes de Décision, Université Laval, Québec City, Québec G1V 0A6, Canada

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  • Alain Martel

    Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), Université de Montréal, Montréal, Quebéc H3C 3J7, Canada, and Faculté des Sciences de l'Administration, Département Opérations et Systémes de Décision, Université Laval, Québec City, Québec G1V 0A6, Canada

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  • Soumia Ichoua

    Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), Université de Montréal, Montréal, Quebéc H3C 3J7, Canada, and Department of Computer Science and Engineering, Johnson C. Smith University, Charlotte, North Carolina 28216

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Published Online:https://doi.org/10.1287/trsc.1090.0307

This paper studies a stochastic multiperiod location-transportation problem (SMLTP) characterized by multiple transportation options, multiple demand periods, and a stochastic demand. We consider the determination of the number and location of the depots required to satisfy customer demand as well as the mission of these depots in terms of the subset of customers they must supply. The problem is formulated as a stochastic program with recourse, and a hierarchical heuristic solution approach is proposed. It incorporates a tabu search procedure, an approximate route length formula, and a modified procedure of Clarke and Wright (Clarke, G., J. W. Wright. 1964. Scheduling of vehicles from a central depot to a number of delivery points. Oper. Res.12 568–581). Three neighbourhood exploration strategies are proposed and compared with extensive experiments based on realistic problems.

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