IDEAS home Printed from https://ideas.repec.org/a/ids/eujine/v14y2020i4p485-516.html
   My bibliography  Save this article

Maximising reward from a team of surveillance drones: a simheuristic approach to the stochastic team orienteering problem

Author

Listed:
  • Javier Panadero
  • Angel A. Juan
  • Christopher Bayliss
  • Christine Currie

Abstract

We consider the problem of routing a team of unmanned aerial vehicles (drones) being used to take surveillance observations of target locations, where the value of information at each location is different and not all locations need be visited. As a result, this problem can be described as a stochastic team orienteering problem (STOP), in which travel times are modelled as random variables following generic probability distributions. The orienteering problem is a vehicle-routing problem in which each of a set of customers can be visited either just once or not at all within a limited time period. In order to solve this STOP, a simheuristic algorithm based on an original and fast heuristic is developed. This heuristic is then extended into a variable neighbourhood search (VNS) metaheuristic. Finally, simulation is incorporated into the VNS framework to transform it into a simheuristic algorithm, which is then employed to solve the STOP. [Received 5 January 2019; Revised 15 June 2019; Accepted 13 October 2019]

Suggested Citation

  • Javier Panadero & Angel A. Juan & Christopher Bayliss & Christine Currie, 2020. "Maximising reward from a team of surveillance drones: a simheuristic approach to the stochastic team orienteering problem," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 14(4), pages 485-516.
    • Handle: RePEc:ids:eujine:v:14:y:2020:i:4:p:485-516
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=108581
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Angel A. Juan & Peter Keenan & Rafael Martí & Seán McGarraghy & Javier Panadero & Paula Carroll & Diego Oliva, 2023. "A review of the role of heuristics in stochastic optimisation: from metaheuristics to learnheuristics," Annals of Operations Research, Springer, vol. 320(2), pages 831-861, January.
    2. Elnaz Ghorbani & Tristan Fluechter & Laura Calvet & Majsa Ammouriova & Javier Panadero & Angel A. Juan, 2023. "Optimizing Energy Consumption in Smart Cities’ Mobility: Electric Vehicles, Algorithms, and Collaborative Economy," Energies, MDPI, vol. 16(3), pages 1-19, January.
    3. Leandro do C. Martins & Rafael D. Tordecilla & Juliana Castaneda & Angel A. Juan & Javier Faulin, 2021. "Electric Vehicle Routing, Arc Routing, and Team Orienteering Problems in Sustainable Transportation," Energies, MDPI, vol. 14(16), pages 1-30, August.
    4. Rocio de la Torre & Bhakti S. Onggo & Canan G. Corlu & Maria Nogal & Angel A. Juan, 2021. "The Role of Simulation and Serious Games in Teaching Concepts on Circular Economy and Sustainable Energy," Energies, MDPI, vol. 14(4), pages 1-21, February.
    5. Mohammad Peyman & Pedro J. Copado & Rafael D. Tordecilla & Leandro do C. Martins & Fatos Xhafa & Angel A. Juan, 2021. "Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems," Energies, MDPI, vol. 14(19), pages 1-26, October.
    6. Omer Ozkan & Sezgin Kilic, 2023. "UAV routing by simulation-based optimization approaches for forest fire risk mitigation," Annals of Operations Research, Springer, vol. 320(2), pages 937-973, January.

    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:ids:eujine:v:14:y:2020:i:4:p:485-516. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=210 .

    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.