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On the optimal design of the disassembly and recovery processes
Authors:A Xanthopoulos  E Iakovou
Institution:1. School of Mechanical Engineering, Xi''an University of Science and Technology, Xi''an, China;2. School of Intelligent Systems Science and Engineering, Jinan University (Zhuhai Campus), Zhuhai, 519070, China;3. Department of Systems & Industrial Engineering, University of Arizona, Tucson, AZ 85721, USA;4. School of Computer Science, Liaocheng University, Liaocheng, 252059, China;5. School of Mechanical Engineering, Hefei University of Technology, Hefei, China;6. The State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China;7. Environmental and Ecological Engineering, Purdue University, West Lafayette, IN 47907, USA
Abstract:This paper tackles the problem of the optimal design of the recovery processes of the end-of-life (EOL) electric and electronic products, with a special focus on the disassembly issues. The objective is to recover as much ecological and economic value as possible, and to reduce the overall produced quantities of waste. In this context, a medium-range tactical problem is defined and a novel two-phased algorithm is presented for a remanufacturing-driven reverse supply chain. In the first phase, we propose a multicriteria/goal-programming analysis for the identification and the optimal selection of the most ‘desirable’ subassemblies and components to be disassembled for recovery, from a set of different types of EOL products. In the second phase, a multi-product, multi-period mixed-integer linear programming (MILP) model is presented, which addresses the optimization of the recovery processes, while taking into account explicitly the lead times of the disassembly and recovery processes. Moreover, a simulation-based solution approach is proposed for capturing the uncertainties in reverse logistics. The overall approach leads to an easy-to-use methodology that could support effectively middle level management decisions. Finally, the applicability of the developed methodology is illustrated by its application on a specific case study.
Keywords:
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