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Jul.  2021

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2021  >  8(7): 1308-1324
X. W. Guo, M. C. Zhou, A. Abusorrah, F. Alsokhiry, and K. Sedraoui, "Disassembly Sequence Planning: A Survey," IEEE/CAA J. Autom. Sinica, vol. 8, no. 7, pp. 1308-1324, Jul. 2021. doi: 10.1109/JAS.2020.1003515
Citation: X. W. Guo, M. C. Zhou, A. Abusorrah, F. Alsokhiry, and K. Sedraoui, "Disassembly Sequence Planning: A Survey," IEEE/CAA J. Autom. Sinica, vol. 8, no. 7, pp. 1308-1324, Jul. 2021. doi: 10.1109/JAS.2020.1003515
shu

Disassembly Sequence Planning: A Survey

doi: 10.1109/JAS.2020.1003515
  • 1.

    College of Computer and Communication Engineering, Liaoning Shihua University, Fushun 113001, China

  • 2.

    Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark NJ 07102 USA

  • 3.

    Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah 21481, Saudi Arabia

  • 4.

    Department of Electrical and Computer Engineering, Faculty of Engineering, and the Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah 21481, Saudi Arabia

Funds:  This work was supported in part by the Research Foundation of China (L2019027), Liaoning Revitalization Talents Program (XLYC1907166), and the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah (KEP-2-135-39)
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    Abstract
  • It is well-recognized that obsolete or discarded products can cause serious environmental pollution if they are poorly be handled. They contain reusable resource that can be recycled and used to generate desired economic benefits. Therefore, performing their efficient disassembly is highly important in green manufacturing and sustainable economic development. Their typical examples are electronic appliances and electromechanical/mechanical products. This paper presents a survey on the state of the art of disassembly sequence planning. It can help new researchers or decision makers to search for the right solution for optimal disassembly planning. It reviews the disassembly theory and methods that are applied for the processing, repair, and maintenance of obsolete/discarded products. This paper discusses the recent progress of disassembly sequencing planning in four major aspects: product disassembly modeling methods, mathematical programming methods, artificial intelligence methods, and uncertainty handling. This survey should stimulate readers to be engaged in the research, development and applications of disassembly and remanufacturing methodologies in the Industry 4.0 era.

     

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    Highlights

    • It attempts to discuss journal and conference papers with an emphasis on recent work. Scholars and engineers investigate DSP with a diversity of interests. This review presents an analysis of disassembly sequences to help decision makers engage in product recovery and reuse activities. Over 100 scientific papers published between 2007 and 2020 are reviewed in this work.
    • Modeling methods include graphical ones such as graph-based modeling, AND/OR graph, Petri nets and matrix-based models. All the possible disassembly sequences can be generated by using these modeling methods. This is followed by the search for reasonable and optimized disassembly sequences, which can be done according to heuristics. In addition, the advantages and disadvantages of these modeling methods are summarized.
    • Mathematical programming and optimization methods are presented, which are the main content of this paper. AI methods are also discussed to solve a DSP problem, for example, genetic algorithm (GA), ant colony optimizer, scatter search (SS) and particle swarm optimizer. Their advantages and disadvantages are summarized in this work.
    • In an EOL product, uncertainty plays an important role. Physical properties of the connections are considered, for instance, friction, or simply by restricting the number of degrees of freedom. Uncertainty theory and methods are discussed and used to solve a stochastic DSP problem.

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