IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v55y2017i10p2990-3007.html
   My bibliography  Save this article

Line- conversion towards reducing worker(s) without increasing makespan: models, exact and meta-heuristic solutions

Author

Listed:
  • Yang Yu
  • Wei Sun
  • Jiafu Tang
  • Ikou Kaku
  • Junwei Wang

Abstract

Compared with the traditional assembly line, seru production can reduce worker(s) and decrease makespan. However, when the two objectives are considered simultaneously, Pareto-optimal solutions may save manpower but increase makespan. Therefore, we formulate line-seru conversion towards reducing worker(s) without increasing makespan and develop exact and meta-heuristic algorithms for the different scale instances. Firstly, we analyse the distinct features of the model. Furthermore, according to the feature of the solution space, we propose two exact algorithms to solve the small to medium-scale instances. The first exact algorithm searches the solution space from more workers to fewer workers. The second exact algorithm searches the solution space from fewer workers to more workers. The two exact algorithms search a part of solution space to obtain the optimal solution of reducing worker(s) without increasing makespan. According to the variable length of the feasible solutions, we propose a variable-length encoding heuristic algorithm for the large-scale instances. Finally, we use the extensive experiments to evaluate the performance of the proposed algorithms and to investigate some managerial insights on when and how to reduce worker(s) without increasing makespan by line-seru conversion.

Suggested Citation

  • Yang Yu & Wei Sun & Jiafu Tang & Ikou Kaku & Junwei Wang, 2017. "Line- conversion towards reducing worker(s) without increasing makespan: models, exact and meta-heuristic solutions," International Journal of Production Research, Taylor & Francis Journals, vol. 55(10), pages 2990-3007, May.
  • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:10:p:2990-3007
    DOI: 10.1080/00207543.2017.1284359
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2017.1284359
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/00207543.2017.1284359?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ye Wang & Jiafu Tang, 2022. "Optimized skill configuration for the seru production system under an uncertain demand," Annals of Operations Research, Springer, vol. 316(1), pages 445-465, September.

    More about this item

    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:taf:tprsxx:v:55:y:2017:i:10:p:2990-3007. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TPRS20 .

    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.