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A Q-learning based selective disassembly planning service in the cloud based remanufacturing system for WEEE
KTH, School of Industrial Engineering and Management (ITM), Production Engineering.ORCID iD: 0000-0001-8679-8049
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2014 (English)In: ASME 2014 International Manufacturing Science and Engineering Conference, MSEC 2014 Collocated with the JSME 2014 International Conference on Materials and Processing and the 42nd North American Manufacturing Research Conference, ASME Press, 2014Conference paper, Published paper (Refereed)
Abstract [en]

Cloud based approach for remanufacturing is becoming a new technical solution for sustainable management of Waste Electrical and Electronic Equipment (WEEE). This paper presents a service-oriented framework of a Cloud Based Remanufacturing System (CBRS) for WEEE. In remanufacturing of WEEE, disassembly plays an important role. However, complete disassembly is rarely an ideal solution due to the high disassembly cost, with the increasing customization and diversity, and more complex assembly processes of Electrical and Electronic Equipment (EEE). Selective disassembly focusing on disassembling only a few selected components is a better choice. In this paper, a Q-Learning based Selective Disassembly Planning (QL-SDP) approach embedded with a multi-criteria decision making model is developed. The multi-criteria decision making model is built according to the legislative and economic considerations of specific stakeholders of WEEE. And the QLSDP approach is used to achieve optimized selective disassembly planning. An implementation example has been used to verify and demonstrate the effectiveness and robustness of the approach. The developed QL-SDP approach is designed as a service implemented in the presented CBRS for WEEE.

Place, publisher, year, edition, pages
ASME Press, 2014.
Keyword [en]
Cloud based remanufacturing, Q-Learning, Remanufacturing services, Selective disassembly planning, Waste electrical and electronic equipment (WEEE)
National Category
Other Engineering and Technologies
Identifiers
URN: urn:nbn:se:kth:diva-158114DOI: 10.1115/MSEC2014-4008ISI: 000361249700051Scopus ID: 2-s2.0-84908888858ISBN: 978-079184580-6 (print)OAI: oai:DiVA.org:kth-158114DiVA: diva2:775089
Conference
ASME 2014 International Manufacturing Science and Engineering Conference, MSEC 2014 Collocated with the JSME 2014 International Conference on Materials and Processing and the 42nd North American Manufacturing Research Conference, Detroit, United States, 9 June 2014 through 13 June 2014
Note

QC 20141230

Available from: 2014-12-30 Created: 2014-12-22 Last updated: 2016-02-19Bibliographically approved

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Wang, Lihui

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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