Journal
JOURNAL OF CLEANER PRODUCTION
Volume 220, Issue -, Pages 1088-1100Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2019.01.086
Keywords
Sustainable development; Low-carbon economy; Automobile reverse logistics; Product take-back China; Selection of take-back pattern; Grey method; Fuzzy method
Categories
Funding
- Science and technology development project of Jilin Province [20180101060JC, 20180101058JC]
- Natural Science Foundation of China [51775238]
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As the number of scrap cars increases rapidly, the management of their reverse logistics (RL) process has become an urgent issue for decision-makers in contemporary China. The process, capable of generating profits as well as minimizing negative environmental effects, mainly includes products acquisition, collection, inspection and disposition. Although the selection of take-back patterns is an essential part in the process, it is not well addressed by existing studies. Involving numerous related factors, the evaluation and selection of the take-back patterns of scrap cars are a comprehensive problem that can be formulated as a multi-criteria decision making (MCDM) problem. In this paper, we propose a complete method to solve it systematically. Three patterns quite suitable for China are presented and a novel criteria system is developed for choosing the best one. The unavoidable uncertainty in practical situation is also considered in this work. We integrate the grey method and Decision-making and Trial Evaluation Laboratory (grey DEMATEL) to determine the weights of nine criteria, and a fuzzy method and Vise Kriterijumska Optimizacija I Kompromisno Resenje (fuzzy VIKOR) are adopted to rank the three patterns based on expert evaluations. Sensitivity analysis is also used to test and verify the robustness of the proposed method. Finally, we draw a conclusion that the Producer Responsibility Organization Take-back pattern whose main body is formed by manufacturer union organization is most suitable for China. The study results could provide a theoretical guideline for decision-makers in China as well as other developing countries. (C) 2019 Elsevier Ltd. All rights reserved.
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