IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v60y2022i9p2816-2829.html
   My bibliography  Save this article

Efficient Fab facility layout with spine structure using genetic algorithm under various material-handling considerations

Author

Listed:
  • Yong Jin Suh
  • Jin Young Choi

Abstract

The Fabrication (Fab) layout design is a strategic issue and has a significant impact on the operational efficiency of semiconductor manufacturing. This research work was motivated by the actual problem to analyse and disperse the congested material flows of central corridor caused by an automated material-handling system (AMHS) in the spine-structure Fab of S Electronics in Korea, which is currently in mass production. In this paper, we suggest an efficient Fab facility layout determination method using genetic algorithm, while considering the interrelationship between manufacturing processors and AMHS. Specifically, we devise a special fitness function employing traffic congestion penalty for reverse and cross-material flows in addition to the usual material-handling distance. By using numerical experiments, we show the superiority of the suggested approach for reducing the overall distance of congested material handling by decreasing the reverse and cross-flows, which cause traffic congestions in the central corridor and entire Fab as well. We expect that this method is expected to be helpful in solving the Fab process layout problems at the Fab planning stage in the actual industrial field.

Suggested Citation

  • Yong Jin Suh & Jin Young Choi, 2022. "Efficient Fab facility layout with spine structure using genetic algorithm under various material-handling considerations," International Journal of Production Research, Taylor & Francis Journals, vol. 60(9), pages 2816-2829, May.
  • Handle: RePEc:taf:tprsxx:v:60:y:2022:i:9:p:2816-2829
    DOI: 10.1080/00207543.2021.1904159
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2021.1904159
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/00207543.2021.1904159?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. Chien-Chih Wang & Yi-Ying Yang, 2023. "A Machine Learning Approach for Improving Wafer Acceptance Testing Based on an Analysis of Station and Equipment Combinations," Mathematics, MDPI, vol. 11(7), pages 1-14, March.

    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:60:y:2022:i:9:p:2816-2829. 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.