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A multi-stage stochastic integer programming approach for a multi-echelon lot-sizing problem with returns and lost sales

Abstract : We consider an uncapacitated multi-item multi-echelon lot-sizing problem within a remanufacturing system involving three production echelons: disassembly, refurbishing and reassembly. We seek to plan the production activities on this system over a multi-period horizon. We consider a stochastic environment, in which the input data of the optimization problem are subject to uncertainty. We propose a multi-stage stochastic integer programming approach relying on scenario trees to represent the uncertain information structure and develop a branch-and-cut algorithm in order to solve the resulting mixed-integer linear program to optimality. This algorithm relies on a new set of tree inequalities obtained by combining valid inequalities previously known for each individual scenario of the scenario tree. These inequalities are used within a cutting-plane generation procedure based on a heuristic resolution of the corresponding separation problem. Computational experiments carried out on randomly generated instances show that the proposed branch-and-cut algorithm performs well as compared to the use of a stand-alone mathematical solver. Finally, rolling horizon simulations are carried out to assess the practical performance of the multi-stage stochastic planning model with respect to a deterministic model and a two-stage stochastic planning model.
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Submitted on : Monday, June 22, 2020 - 4:45:47 PM
Last modification on : Wednesday, March 16, 2022 - 3:50:58 AM


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Franco Quezada, Céline Gicquel, Safia Kedad-Sidhoum, Dong Quan Vu. A multi-stage stochastic integer programming approach for a multi-echelon lot-sizing problem with returns and lost sales. Computers and Operations Research, Elsevier, 2020, 116, pp.104865. ⟨10.1016/j.cor.2019.104865⟩. ⟨hal-02470310⟩



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