IDEAS home Printed from https://ideas.repec.org/h/spr/spochp/978-3-031-58919-5_9.html
   My bibliography  Save this book chapter

A Greedy and Variable Neighborhood Search Metaheuristic Approach for the Cumulative Unmanned Aerial Vehicle Routing Problem

In: Disruptive Technologies and Optimization Towards Industry 4.0 Logistics

Author

Listed:
  • Nikolaos A. Kyriakakis

    (Technical University of Crete)

  • Themistoklis Stamadianos

    (Technical University of Crete)

  • Magdalene Marinaki

    (Technical University of Crete)

  • Yannis Marinakis

    (Technical University of Crete)

Abstract

The Cumulative Unmanned Aerial Vehicle Routing Problem (CUAVRP) has been proposed for addressing operations in which an area of interest needs to be covered using a fleet of drones with the minimum possible latency. Transforming the underlying coverage path planning problem into a vehicle routing problem allows for different objectives and constraints to be considered in the model. The CUAVRP uses the Cumulative Capacitated Vehicle Routing Problem cost model, which focuses on minimizing the sum of arrival times at all points of the area of interest. Thus, the CUAVRP is well-suited for search and rescue UAV missions, where latency is the most prominent indicator of success. In this paper, a Greedy and Variable Neighborhood Search (GnVNS) metaheuristic algorithm is implemented by combining greedy destroy-repair operators with VNS. Two variants of the destroy operator are implemented and tested on the 25 CUAVRP benchmark instances from the literature. Both GnVNS variants were able to match 9 previously published best-known values and provided new best solutions for multiple instances.

Suggested Citation

  • Nikolaos A. Kyriakakis & Themistoklis Stamadianos & Magdalene Marinaki & Yannis Marinakis, 2024. "A Greedy and Variable Neighborhood Search Metaheuristic Approach for the Cumulative Unmanned Aerial Vehicle Routing Problem," Springer Optimization and Its Applications, in: Athanasia Karakitsiou & Athanasios Migdalas & Panos M. Pardalos (ed.), Disruptive Technologies and Optimization Towards Industry 4.0 Logistics, pages 247-265, Springer.
    • Handle: RePEc:spr:spochp:978-3-031-58919-5_9
    DOI: 10.1007/978-3-031-58919-5_9
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spochp:978-3-031-58919-5_9. 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.