IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v236y2022i5p647-660.html
   My bibliography  Save this article

Dynamic and adaptive grouping maintenance strategies: New scalable optimization algorithms

Author

Listed:
  • Maria Hanini
  • Selma Khebbache
  • Laurent Bouillaut
  • Makhlouf Hadji

Abstract

This paper focuses on new efficient and adaptive optimization algorithms to cope with the maintenance grouping problem for series, parallel, and complex systems. We propose a Particle Swarm Optimization approach to cope with small and medium problem sizes, and that will be used to benchmark existing heuristic solutions such as Genetic Algorithms. To address scalability and adaptability issues, we propose a new dynamic optimization algorithm based on a clustering technique. This clustering-based solution is formulated using an Integer Linear Programing approach to guarantee the convergence to global optimal solutions of the considered problem. We show the performance of the proposed approaches with a clear advantage to the clustering-based algorithm that we recommend for large industrial systems.

Suggested Citation

  • Maria Hanini & Selma Khebbache & Laurent Bouillaut & Makhlouf Hadji, 2022. "Dynamic and adaptive grouping maintenance strategies: New scalable optimization algorithms," Journal of Risk and Reliability, , vol. 236(5), pages 647-660, October.
    • Handle: RePEc:sae:risrel:v:236:y:2022:i:5:p:647-660
    DOI: 10.1177/1748006X211049924
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X211049924
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1748006X211049924?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
    ---><---

    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:sae:risrel:v:236:y:2022:i:5:p:647-660. 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: SAGE Publications (email available below). General contact details of provider: .

    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.