Selective disassembly planning for waste electrical and electronic equipment with case studies on liquid crystaldisplays

  • Authors:
  • W. D. Li;K. Xia;L. Gao;K. -M. Chao

  • Affiliations:
  • Faculty of Engineering and Computing, Coventry University, UK;State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, China;State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, China;Faculty of Engineering and Computing, Coventry University, UK

  • Venue:
  • Robotics and Computer-Integrated Manufacturing
  • Year:
  • 2013

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Abstract

Waste Electrical and Electronic Equipments (WEEEs) are one of the most significant waste streams in modern societies. In the past decade, disassembly of WEEE to support remanufacturing and recycling has been growingly adopted by industries. With the increasing customisation and diversity of Electrical and Electronic Equipment (EEE) and more complex assembly processes, full disassembly of WEEE is rarely an ideal solution due to high disassembly cost. Selective disassembly, which prioritises operations for partial disassembly according to the legislative and economic considerations of specific stakeholders, is becoming an important but still a challenging research topic in recent years. In order to address the issue effectively, in this paper, a Particle Swarm Optimisation (PSO)-based selective disassembly planning method embedded with customisable decision making models and a novel generic constraint handling algorithm has been developed. With multi-criteria and adaptive decision making models, the developed method is flexible to handle WEEE to meet the various requirements of stakeholders. Based on the generic constraint handling and intelligent optimisation algorithms, the developed research is capable to process complex constraints and achieve optimised selective disassembly plans. Industrial cases on Liquid Crystal Display (LCD) televisions have been used to verify and demonstrate the effectiveness and robustness of the research in different application scenarios.