IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v338y2024i1d10.1007_s10479-023-05661-3.html
   My bibliography  Save this article

Efficient solutions to the m-machine robust flow shop under budgeted uncertainty

Author

Listed:
  • Mario Levorato

    (Universidade Federal Fluminense
    Avignon Université
    Petrobras)

  • David Sotelo

    (Petrobras)

  • Rosa Figueiredo

    (Avignon Université)

  • Yuri Frota

    (Universidade Federal Fluminense)

Abstract

This work presents two solution methods for the m-machine robust permutation flow shop problem with processing time uncertainty. The goal is to minimize the makespan of the worst-case scenario by utilizing an approach based on budgeted uncertainty, in which only a subset of operations will reach their worst-case processing time values. To obtain efficient solutions to this problem, we first extend an existing two-machine worst-case procedure, based on dynamic programming, generalizing it to m machines. The worst-case calculation is then incorporated into two proposed solution methods: an exact column-and-constraint generation algorithm and a GRASP metaheuristic. Based on experiments with four sets of literature-based instances, empirical results demonstrate the ability of the GRASP to efficiently produce an optimal or near-optimal solution in most cases.

Suggested Citation

  • Mario Levorato & David Sotelo & Rosa Figueiredo & Yuri Frota, 2024. "Efficient solutions to the m-machine robust flow shop under budgeted uncertainty," Annals of Operations Research, Springer, vol. 338(1), pages 765-799, July.
    • Handle: RePEc:spr:annopr:v:338:y:2024:i:1:d:10.1007_s10479-023-05661-3
    DOI: 10.1007/s10479-023-05661-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-023-05661-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-023-05661-3?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.

    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:annopr:v:338:y:2024:i:1:d:10.1007_s10479-023-05661-3. 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.