Dynamic Facility Location with Generalized Modular Capacities

Sanjay Dominik Jena (), Jean-François Cordeau () and Bernard Gendron ()
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Sanjay Dominik Jena: Centre interuniversitaire de recherche sur les réseaux d’entreprise, la logistique et le transport (CIRRELT); and Département d’informatique et de recherche opérationnelle, Université de Montréal, Montréal, Québec H3T 1J4, Canada
Jean-François Cordeau: Centre interuniversitaire de recherche sur les réseaux d’entreprise, la logistique et le transport (CIRRELT); and Canada Research Chair in Logistics and Transportation, HEC Montréal, Montréal, Québec H3T 2A7, Canada
Bernard Gendron: Centre interuniversitaire de recherche sur les réseaux d’entreprise, la logistique et le transport (CIRRELT); and Département d’informatique et de recherche opérationnelle, Université de Montréal, Montréal, Québec H3T 1J4, Canada

Transportation Science, 2015, vol. 49, issue 3, 484-499

Abstract: Location decisions are frequently subject to dynamic aspects such as changes in customer demand. Often, flexibility regarding the geographic location of facilities, as well as their capacities, is the only solution to such issues. Even when demand can be forecast, finding the optimal schedule for the deployment and dynamic adjustment of capacities remains a challenge, especially when the cost structure for these adjustments is complex. In this paper, we introduce a unifying model that generalizes existing formulations for several dynamic facility location problems and provides stronger linear programming relaxations than the specialized formulations. In addition, the model can address facility location problems where the costs for capacity changes are defined for all pairs of capacity levels. To the best of our knowledge, this problem has not been addressed in the literature. We apply our model to special cases of the problem with capacity expansion and reduction or temporary facility closing and reopening. We prove dominance relationships between our formulation and existing models for the special cases. Computational experiments on a large set of randomly generated instances with up to 100 facility locations and 1,000 customers show that our model can obtain optimal solutions in shorter computing times than the existing specialized formulations.

Keywords: mixed-integer programming; facility location; modular capacities (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (22)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ortrsc:v:49:y:2015:i:3:p:484-499

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