IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v58y2020i9p2696-2711.html
   My bibliography  Save this article

Large neighbourhood search based on mixed integer programming and ant colony optimisation for car sequencing

Author

Listed:
  • Dhananjay Thiruvady
  • Kerri Morgan
  • Amiza Amir
  • Andreas T. Ernst

Abstract

We investigate the problem of scheduling a sequence of cars to be placed on an assembly line. Stations, along the assembly line install options (e.g. air conditioning), but have limited capacities, and hence cars requiring the same options need to be distributed far enough apart. The desired separation is not always feasible, leading to an optimisation problem that minimises the violation of the ideal separation requirements. In order to solve the problem, we use a large neighbourhood search (LNS) based on mixed integer programming (MIP). The search is implemented as a sliding window, by selecting overlapping subsequences of manageable sizes, which can be solved efficiently. Our experiments show that, with LNS, substantial improvements in solution quality can be found.

Suggested Citation

  • Dhananjay Thiruvady & Kerri Morgan & Amiza Amir & Andreas T. Ernst, 2020. "Large neighbourhood search based on mixed integer programming and ant colony optimisation for car sequencing," International Journal of Production Research, Taylor & Francis Journals, vol. 58(9), pages 2696-2711, May.
    • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:9:p:2696-2711
    DOI: 10.1080/00207543.2019.1630765
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2019.1630765
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207543.2019.1630765?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.

    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:58:y:2020:i:9:p:2696-2711. 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.