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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
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    Citations

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    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. Henri Meeß & Michael Herzog & Enes Alp & Bernd Kuhlenkötter, 2024. "Evolutionary algorithms for a simheuristic optimization of the product-service system design," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3235-3257, October.
    5. Shiri, Davood & Akbari, Vahid & Hassanzadeh, Ali, 2024. "The Capacitated Team Orienteering Problem: An online optimization framework with predictions of unknown accuracy," Transportation Research Part B: Methodological, Elsevier, vol. 185(C).
    6. Morandi, Nicola & Leus, Roel & Yaman, Hande, 2024. "The orienteering problem with drones," Other publications TiSEM 593f31f0-7b7b-4069-84ca-8, Tilburg University, School of Economics and Management.
    7. 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.
    8. 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.
    9. 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.

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