IDEAS home Printed from https://ideas.repec.org/a/igg/jamc00/v16y2025i1p1-30.html
   My bibliography  Save this article

Advancing Hybrid Metaheuristics: Evaluating NN-IHGA for Vehicle Routing Problem With Time Windows

Author

Listed:
  • Aysha Sohail

    (King Mongkut's University of Technology, Thonburi, Thailand)

  • Jumpol Polvichai

    (King Mongkut's University of Technology, Thonburi, Thailand)

  • Taninnuch Lamjiak

    (King Mongkut's University of Technology, Thonburi, Thailand)

  • Aye Thant May

    (King Mongkut's University of Technology, Thonburi, Thailand)

Abstract

The vehicle routing problem with time windows is an NP-hard optimization problem vital to logistics and supply chain management. It involves optimizing vehicle routes to serve customers within time windows and capacity limits. This study proposes a hybrid genetic algorithm combining a nearest neighbor-based initialization with advanced mutation operators. The nearest neighbor method ensures high-quality initial solutions by prioritizing proximity and constraints, while multiple mutation operators enhance exploration and exploitation. Tested on the Solomon 100-customer dataset, NN-IHGA outperformed benchmarks, especially on random and mixed datasets, reducing travel costs and vehicle counts. Results highlight NN-IHGA's robustness and adaptability, offering a practical solution for real-world logistics optimization.

Suggested Citation

  • Aysha Sohail & Jumpol Polvichai & Taninnuch Lamjiak & Aye Thant May, 2025. "Advancing Hybrid Metaheuristics: Evaluating NN-IHGA for Vehicle Routing Problem With Time Windows," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global Scientific Publishing, vol. 16(1), pages 1-30, January.
    • Handle: RePEc:igg:jamc00:v:16:y:2025:i:1:p:1-30
    as

    Download full text from publisher

    File URL: https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJAMC.387961
    Download Restriction: no
    ---><---

    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:igg:jamc00:v:16:y:2025:i:1:p:1-30. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.