IDEAS home Printed from https://ideas.repec.org/h/spr/advbcp/978-94-6463-770-0_86.html

Modeling and Optimization of U-shaped Sequence-dependent Disassembly Line Balancing Problem

In: Proceedings of the 2025 3rd International Conference on Digital Economy and Management Science (CDEMS 2025)

Author

Listed:
  • Jia Liu

    (Qingdao University of Technology, Business School)

  • Lujing Wang

    (Qingdao University of Technology, Business School)

  • Shuwei Wang

    (Shandong University of Science and Technology, College of Economics & Management)

Abstract

Disassembly line is the most suitable way for enterprises to disassemble large-scale waste products. However, the disassembly process is complex. It is hard to balance the workload among workstations of line, so designing and balancing the disassembly line is important. Besides, in disassembly process some precedence free parts may interfere with each other. Whenever precedence free tasks interact, their task times will be influenced based on the order in which they are performed. Therefore, in this paper, a multi-objective mathematical model is constructed for U-shaped Sequence-dependent Disassembly Line Balancing Problem (USDDLBP). From efficiency, economic and environmental concerns, the number of open workstations, the smoothing index, and the early disassembly of hazardous and high-demand parts are considered in the disassembly process. The artificial bee colony algorithm is used to solve the problem. The performance of U-shaped and linear disassembly lines is evaluated by benchmark examples, and the calculation results prove the rationality and effectiveness of the presented model for the USDDLBP.

Suggested Citation

  • Jia Liu & Lujing Wang & Shuwei Wang, 2025. "Modeling and Optimization of U-shaped Sequence-dependent Disassembly Line Balancing Problem," Advances in Economics, Business and Management Research, in: Wenke Zang & Chunping Xia (ed.), Proceedings of the 2025 3rd International Conference on Digital Economy and Management Science (CDEMS 2025), pages 760-766, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-770-0_86
    DOI: 10.2991/978-94-6463-770-0_86
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:advbcp:978-94-6463-770-0_86. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.