Joint decision-making on automated disassembly system scheme selection and recovery route assignment using multi-objective meta-heuristic algorithm. Issue 1 (2nd January 2019)

• Record Type: Journal Article
• Authors: Tao, Yiyun; Meng, Kai; Lou, Peihuang; Peng, Xianghui; Qian, Xiaoming
• Is Part Of: International journal of production research. Volume 57:Issue 1(2019)
• Page Start: 124
• Page End: 142
• Publication Date: 2019-01-02
• Subjects: automated disassembly system -- product recovery -- multi-objective optimisation -- Waste Electrical and Electronic Equipment -- disassembly -- sustainable manufacturing; Factory management -- Periodicals; 658.57
• DOI: 10.1080/00207543.2018.1461274
• Languages: English
• ISSNs: 0020-7543

Abstract

Abstract : Green treatment on Waste Electrical and Electronic Equipmenthas increasingly attracted attention due to its significant environmental benefits and potential recovery earnings. Automated disassembly has been regarded as a powerful solution to enable more efficient recovery operations. Although numerous studies have contributed to the issues of disassembly, there are few researches that focus on decision model for selecting disassembly system scheme and recovery route in automated disassembly. In this paper, we propose a two-phase joint decision-making model to address this problem with the goal of balancing disassembly profit with environmental impact. First, we establish a multi-objective optimisation model to obtain the Pareto optimal recovery routes for each automated disassembly system scheme. Both recovery profit and energy consumption are evaluated for multi-station disassembly system. We design a multi-objective hybrid particle swarm optimisation algorithm based on symbiotic evolutionary mechanism to solve the proposed model. Then, we compare the Pareto optimal solutions of all the system schemes using a fuzzy set method and identify the best scheme. Finally, we conduct real case studies on the automated disassembly of different waste electric metres. The results demonstrate the superiority of automated disassembly and validate the effectiveness of our proposed model and algorithm.