Learning-embedded disassembly petri net for process planning

Ying Tang, Meng Chu Zhou

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    12 Scopus citations

    Abstract

    The growing concerns for material resources, energy conservation and landfill capacity have put much pressure on manufacturers, charging them with the responsibility for their outdated products. However, obstacles arise when introducing product/material recovery in the economic landscape due to much uncertainty inherent in the process (e.g., prevailing condition of reclaimed products and the level of human intervention). This paper presents a rigorous model that accounts for such system dynamics in disassembly process planning (DPP), a critical stage to the efficiency of product/material recovery. In particular, this model with the learning capability will be able to: (1) mathematically represent the operational planning of disassembly in the light of uncertainty (i.e., the quality of reclaimed products and the impact of human intervention); (2) accumulate and exploit "knowledge" of system performance via the observation of the process behavior; and (3) dynamically derive a cost-effective disassembly plan.

    Original languageEnglish (US)
    Title of host publication2006 IEEE International Conference on Systems, Man and Cybernetics
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages80-84
    Number of pages5
    ISBN (Print)1424401003, 9781424401000
    DOIs
    StatePublished - Jan 1 2006
    Event2006 IEEE International Conference on Systems, Man and Cybernetics - Taipei, Taiwan, Province of China
    Duration: Oct 8 2006Oct 11 2006

    Publication series

    NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
    Volume1
    ISSN (Print)1062-922X

    Other

    Other2006 IEEE International Conference on Systems, Man and Cybernetics
    Country/TerritoryTaiwan, Province of China
    CityTaipei
    Period10/8/0610/11/06

    All Science Journal Classification (ASJC) codes

    • Engineering(all)

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