IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v63y2025i19p7174-7195.html
   My bibliography  Save this article

A bi-objective optimisation model for the drone scheduling problem in island delivery

Author

Listed:
  • Ying Yang
  • Jiaxin Liu
  • Shuaian Wang

Abstract

Drone-assisted parcel delivery to remote islands is increasingly replacing traditional methods, offering improved efficiency and enhanced service reliability. This paper addresses the drone scheduling problem in island delivery (DSP-ID) by optimising drone delivery routes. In particular, we first introduce a bi-objective mixed-integer linear programming model that concurrently optimises delivery time and energy consumption. To address the model, both a heuristic non-dominated sorting genetic algorithm II (NSGA-II) and an exact augmented ε-constraint method are developed. The efficacy and robustness of the proposed model and algorithms are evaluated through experiments across various scales. Results indicate that both algorithms yield high-quality solutions for DSP-ID in small-scale scenarios. However, as the problem size expands, the performance of the augmented ε-constraint method wanes under time constraints, whereas the NSGA-II consistently delivers high-quality solutions. Additionally, we provide decision-makers with actionable insights for selecting the most effective drone delivery routes.

Suggested Citation

  • Ying Yang & Jiaxin Liu & Shuaian Wang, 2025. "A bi-objective optimisation model for the drone scheduling problem in island delivery," International Journal of Production Research, Taylor & Francis Journals, vol. 63(19), pages 7174-7195, October.
    • Handle: RePEc:taf:tprsxx:v:63:y:2025:i:19:p:7174-7195
    DOI: 10.1080/00207543.2025.2496965
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2025.2496965
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207543.2025.2496965?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

    for a different version of it.

    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:taf:tprsxx:v:63:y:2025:i:19:p:7174-7195. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TPRS20 .

    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.