IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i19p3116-d1760929.html
   My bibliography  Save this article

A Heuristic Guided Genetic Algorithm Applied to Dual Resource Job Shop Scheduling

Author

Listed:
  • Ricardo Magalhães

    (IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal)

  • João M. C. Sousa

    (IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal)

  • Susana M. Vieira

    (IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal)

Abstract

This paper introduces the Knowledge-Guided Genetic Algorithm (KGGA), a hybrid metaheuristic that reimagines crossover as a form of genetic engineering rather than random recombination. By embedding knowledge-guided exploitation principles directly into the crossover operator, KGGA selectively amplifies high-quality genetic material, intensifying the search around promising regions of the solution space. Experimental results on a large scale DRC-FJSSP benchmark show that KGGA outperforms state-of-the-art alternatives—including the Classic Genetic Algorithm (GA), Knowledge-Guided Fruit Fly Optimization Algorithm (KGFOA), and Hybrid Artificial Bee Colony Algorithm (HABCA)—consistently achieving superior solution quality.

Suggested Citation

  • Ricardo Magalhães & João M. C. Sousa & Susana M. Vieira, 2025. "A Heuristic Guided Genetic Algorithm Applied to Dual Resource Job Shop Scheduling," Mathematics, MDPI, vol. 13(19), pages 1-13, September.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:19:p:3116-:d:1760929
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/19/3116/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/13/19/3116/
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    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:gam:jmathe:v:13:y:2025:i:19:p:3116-:d:1760929. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.