A Distributed Service of Selective Disassembly Planning for Waste Electrical and Electronic Equipment with Case Studies on Liquid Crystal Display

  • Weidong LiEmail author
  • K. Xia
  • B. Lu
  • K. M. Chao
  • L. Gao
  • J. X. Yang
Part of the Springer Series in Advanced Manufacturing book series (SSAM)


Waste Electrical and Electronic Equipment (WEEE) 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 customization 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 prioritizes operations for partial disassembly according to the legislative and economic considerations of specific stakeholders, is becoming an important yet still challenging research topic in recent years. In this chapter, a Particle Swarm Optimization (PSO)-based selective disassembly planning method embedded with customizable decision-making models and a novel generic constraint handling algorithm has been developed. With multi-criteria 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 optimization algorithms, the research is capable to process complex constraints and achieve optimized selective plans. Practical cases on Liquid Crystal Display (LCD) televisions have been used to verify and demonstrate the effectiveness of the research in different application scenarios. A distributed environment to deploy the service for remote access and control has been designed to support collaborative work.


Particle Swarm Optimization Particle Swarm Optimization Algorithm Liquid Crystal Display Potential Recovery Disassembly Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research was carried out as a part of the GREENet and CASES projects which are supported by a Marie Curie International Research Staff Exchange Scheme Fellowship within the 7th European Community Framework Programme under the grant agreement No 269122 and No 294931. The chapter reflects only the authors’ views and the Union is not liable for any use that may be made of the information contained therein.

The authors would also appreciate Mr Qiang Peng, the Technical Director of the Guangdong Changhong Electronics Company, Ltd., and his team for their strong support during the project in terms of technical consultancy/discussions and raw data providing/explanations.


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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Weidong Li
    • 1
    Email author
  • K. Xia
    • 2
  • B. Lu
    • 3
  • K. M. Chao
    • 1
  • L. Gao
    • 2
  • J. X. Yang
    • 3
  1. 1.Faculty of Engineering and ComputingCoventry UniversityCoventryUK
  2. 2.The State Key Laboratory of Digital Manufacturing Equipment and TechnologyHuazhong University of Science and TechnologyWuhanChina
  3. 3.Research Centre of Eco-Environmental SciencesChinese Academy of SciencesBeijingChina

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