Optimization and Analysis of a Manufacturing–Remanufacturing–Transport–Warehousing System within a Closed-Loop Supply Chain

Sadok Turki, Stanislav Didukh, Christophe Sauvey and Nidhal Rezg
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Sadok Turki: Industrial Engineering and Production Laboratory of Metz, Lorraine University, Ile du Saulcy, 57045 Metz CEDEX, France
Stanislav Didukh: Industrial Engineering and Production Laboratory of Metz, Lorraine University, Ile du Saulcy, 57045 Metz CEDEX, France
Christophe Sauvey: Industrial Engineering and Production Laboratory of Metz, Lorraine University, Ile du Saulcy, 57045 Metz CEDEX, France
Nidhal Rezg: Industrial Engineering and Production Laboratory of Metz, Lorraine University, Ile du Saulcy, 57045 Metz CEDEX, France

Sustainability, 2017, vol. 9, issue 4, 1-20

Abstract: This paper deals with the optimization of a manufacturing–remanufacturing–transport–warehousing closed-loop supply chain, which is composed of two machines for manufacturing and remanufacturing, manufacturing stock, purchasing warehouse, transport vehicle and recovery inventory. The proposed system takes into account the return of used end-of-life products from the market. Manufactured and re-manufactured products are stored in the manufacturing stock. The used end-of-life products are stored in the recovery inventory for remanufacturing. The vehicle transports products from the manufacturing stock to the purchasing warehouse. The objective of this work is to simultaneously evaluate the optimal capacities of manufacturing stock, purchasing warehouse and the vehicle, as well as the optimal value of returned used end-of-life products. Those four decision variables minimize the total cost function. A discrete flow model, which is supposed to be the most realistic, is used to describe the system. An optimization program, based on a genetic algorithm, is developed to find the decision variables. Numerical results are presented to study the influence of transportation time, unit remanufacturing cost and configuration of the manufacturing/remanufacturing machines on the decision variables.

Keywords: closed-loop supply chain system; reverse logistics; discrete flow model; used end-of-life products; green logistics; genetic algorithm; transportation time (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (27)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:9:y:2017:i:4:p:561-:d:95195

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