Stochastic Dual Dynamic integer Programming for a multi-echelon lot-sizing problem with remanufacturing and lost sales

We consider an uncapacitated 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 assume a stochastic environment, in wh...

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Bibliographic Details
Published in:International Conference on Control, Decision and Information Technologies (Online) pp. 1254 - 1259
Main Authors: Quezada, Franco, Gicquel, Celine, Kedad-Sidhoum, Safia
Format: Conference Proceeding
Language:English
Published: IEEE 01.04.2019
Subjects:
Approximation algorithms
Dynamic programming
Heuristic algorithms
Production
Programming
Stochastic processes
Uncertainty
ISSN:2576-3555
Online Access:Get full text
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Summary:We consider an uncapacitated 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 assume a stochastic environment, in which the input data of the optimization problem are subject to uncertainty. We consider a multi-stage stochastic integer programming approach relying on scenario trees to represent the uncertain information structure and propose a solution method based on an extension of the stochastic dual dynamic programming algorithm. Our results show that this approach can provide good quality solutions for large-size instances in a reasonable time and significantly outperforms the use of a stand-alone mathematical solver.
ISSN:2576-3555
DOI:10.1109/CoDIT.2019.8820709