Modeling and solution approaches for the stochastic two-echelon distribution network design problem

Imen Ben Mohamed 1, 2 Francois Vanderbeck 3, 2 Walid Klibi 1
2 Realopt - Reformulations based algorithms for Combinatorial Optimization
LaBRI - Laboratoire Bordelais de Recherche en Informatique, IMB - Institut de Mathématiques de Bordeaux, Inria Bordeaux - Sud-Ouest
Abstract : In this work, we investigate the design of two-echelon distribution networks where product ows towards end-customers must be directed from an upper layer of platforms to Distribution Centers (DCs) before being routed from DCs to customer's base. This problem involves strategic decisions on the location of a set of intermediate DCs over time, the allocation of the capacity level of these DCs for each planning period, and the two-echelon transportation schema of the network. For this design problem under uncertainty, a multi-period planning horizon is considered where demand varies dynamically from one planning period to the subsequent one. Thus, the design of the two-echelon distribution network under uncertain customers' demand gives rise to a complex multi-stage decisional problem. Using a rolling horizon approach and the partition of the planning horizon into a set of design cycles, we formulated the problem as a multi-cycle two-stage stochastic program with recourse. To solve the obtained model a Benders decomposition is developed and coupled with the sample average approximation method. Extensive numerical tests are conducted to validate the modeling and solution approaches proposed for this design problem.
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Conference papers
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https://hal.inria.fr/hal-01675713
Contributor : François Vanderbeck <>
Submitted on : Tuesday, January 23, 2018 - 9:29:50 AM
Last modification on : Monday, June 24, 2019 - 12:20:03 PM

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  • HAL Id : hal-01675713, version 1

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Imen Ben Mohamed, Francois Vanderbeck, Walid Klibi. Modeling and solution approaches for the stochastic two-echelon distribution network design problem. IFORS 2017 - 21st Conference of the International Federation of Operational Research Societies, Jul 2017, Quebec, Canada. ⟨hal-01675713⟩

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