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ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.17 No.3 pp.417-433
DOI : https://doi.org/10.7232/iems.2018.17.3.417

A Multi-Stage Stochastic Mixed-Integer Linear Programming to Design an Integrated Production-Distribution Network under Stochastic Demands

Mohammad Derakhshi, Seyed Taghi Akhavan Niaki*, Seyed Armin Akhavan Niaki
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
Department of Statistics, Eberly College of Arts and Sciences, West Virginia University, Morgantown
*Corresponding Author, E-mail: Niaki@Sharif.edu

Abstract

Supply chain management has gained much interest from researchers and practitioners in recent years. Proposing practical models that efficiently address different aspects of the supply chain is a difficult challenge. This research investigates an integrated production-distribution supply chain problem. The developed model incorporates parties with a specified number of processes to obtain raw materials from the suppliers in order to convert them to semi and final products. These products are then distributed through warehouses to end-distributors having uncertain demands. This uncertainty is captured as a dynamic stochastic data process during the planning horizon and is modeled into a multi-stage stochastic mixed integer linear program using a scenario tree approach. For large-size instances, a hybrid exact-approximate algorithm is proposed, where its effectiveness is assessed via several numerical cases. Furthermore, the model is generalized to its bi-objective version by considering the accessibility of the products based on the safety stock policy of the companies involved. In the end, an existing algorithm is combined with the ε-constraint method to obtain an approximate Pareto front.

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