IDEAS home Printed from https://ideas.repec.org/a/spr/snopef/v5y2024i1d10.1007_s43069-023-00283-8.html
   My bibliography  Save this article

An Empirical Analysis of a Set of Hybrid Heuristics for the Solution of the Resource Leveling Problem

Author

Listed:
  • Marinos Aristotelous

    (University of Patras)

  • Andreas C. Nearchou

    (University of Patras)

Abstract

Consideration is given to the heuristic solution of the resource leveling problem (RLP) in project scheduling with limited resources. The objective is to minimize the changes in the level of resource usage from period to period over the planning horizon of the project while keeping the project duration fixed. First, we present two novel greedy schedule algorithms for the RLP solution. The performance of the proposed algorithms is investigated as low-level hybrids in the context of three famous population-based heuristics, namely differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO). Then, we additionally present two new high-level hybridization schemes (HS), referred to herein as parallel and serial HS, respectively, which combine DE, GA, and PSO in a single hybrid solution algorithm. Detailed experimentation over known complex datasets measures the efficiency of the new hybrids. Statistical analysis employed rank the hybrids according to their solution efficiency. Moreover, comparisons between the developed best hybrid and commercial project management software show a substantial higher performance for the former over real-world construction projects.

Suggested Citation

  • Marinos Aristotelous & Andreas C. Nearchou, 2024. "An Empirical Analysis of a Set of Hybrid Heuristics for the Solution of the Resource Leveling Problem," SN Operations Research Forum, Springer, vol. 5(1), pages 1-29, March.
    • Handle: RePEc:spr:snopef:v:5:y:2024:i:1:d:10.1007_s43069-023-00283-8
    DOI: 10.1007/s43069-023-00283-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s43069-023-00283-8
    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/s43069-023-00283-8?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:snopef:v:5:y:2024:i:1:d:10.1007_s43069-023-00283-8. 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.