Production planning of a hybrid manufacturing–remanufacturing system under uncertainty within a closed-loop supply chain

• Published in: International journal of production economics Vol. 135; no. 1; pp. 81 - 93
• Main Authors: Kenné, Jean-Pierre, Dejax, Pierre, Gharbi, Ali
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 2012; Elsevier Sequoia S.A
• Subjects: Closed-loop supply chains; Control theory; Dynamic programming; Economics; Inventories; Inventory control; Manufacturing; Manufacturing/remanufacturing; Mathematical analysis; Mathematical models; Networks; Numerical methods; Optimal control; Production planning; Remanufacturing; Reverse logistics; Stochastic dynamic programming; Stockpiling; Studies; Supply chain management; Uncertainty; Production planning; Optimal control; Numerical methods; Reverse logistics; Stochastic dynamic programming; Closed-loop supply chains; Manufacturing/remanufacturing
• ISSN: 0925-5273; 1873-7579; 1873-7579
• DOI: 10.1016/j.ijpe.2010.10.026

This paper deals with the production planning and control of a single product involving combined manufacturing and remanufacturing operations within a closed-loop reverse logistics network with machines subject to random failures and repairs. While consumers traditionally dispose of products at the end of their life cycle, recovery of the used products may be economically more attractive than disposal, while remanufacturing of the products also pursues sustainable development goals. Three types of inventories are involved in this network. The manufactured and remanufactured items are stored in the first and second inventories. The returned products are collected in the third inventory and then remanufactured or disposed of. The objective of this research is to propose a manufacturing/remanufacturing policy that would minimize the sum of the holding and backlog costs for manufacturing and remanufacturing products. The decision variables are the production rates of the manufacturing and the remanufacturing machines. The optimality conditions are developed using the optimal control theory based on stochastic dynamic programming. A computational algorithm, based on numerical methods, is used for solving the optimal control problem. Finally, a numerical example and a sensitivity analysis are presented to illustrate the usefulness of the proposed approach. The structure of the optimal control policy is discussed depending on the value of costs and parameters and extensions to more complex reverse logistics networks are discussed.