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Routing Drones in Smart Cities: A Biased-Randomized Algorithm for Solving the Team Orienteering Problem in Real Time

  • Identification data

    Identifier: imarina:6311372
    Handle: https://hdl.handle.net/20.500.11797/imarina6311372
    Authors:
    Juan AAFreixes APanadero JSerrat CEstrada-Moreno A
    Abstract:
    © 2020 The Authors. Published by Elsevier B.V. The concepts of unmanned aerial vehicles and self-driving vehicles are gaining relevance inside the smart city environment. This type of vehicles might use ultra-reliable telecommunication systems, Internet-based technologies, and navigation satellite services to decide about the routes they must follow to efficiently accomplish their mission and reach their destinations in due time. When working in teams of vehicles, there is a need to coordinate their routing operations. When some unexpected events occur in the city (e.g., after a traffic accident, a natural disaster, or a terrorist attack), coordination among vehicles might need to be done in real-time. Using the team orienteering problem as an illustrative case scenario, this paper analyzes how the combined use of extremely fast biased-randomized heuristics and parallel computing allows for 'agile' optimization of routing plans for drones and other autonomous vehicles.

  • Others:

    Author, as appears in the article.: Juan AA; Freixes A; Panadero J; Serrat C; Estrada-Moreno A
    Department: Enginyeria Informàtica i Matemàtiques
    e-ISSN: 2352-1465
    URV's Author/s: Estrada Moreno, Alejandro
    Keywords: Unmanned aerial vehicles Team orienteering problem Smart cities
    Abstract: © 2020 The Authors. Published by Elsevier B.V. The concepts of unmanned aerial vehicles and self-driving vehicles are gaining relevance inside the smart city environment. This type of vehicles might use ultra-reliable telecommunication systems, Internet-based technologies, and navigation satellite services to decide about the routes they must follow to efficiently accomplish their mission and reach their destinations in due time. When working in teams of vehicles, there is a need to coordinate their routing operations. When some unexpected events occur in the city (e.g., after a traffic accident, a natural disaster, or a terrorist attack), coordination among vehicles might need to be done in real-time. Using the team orienteering problem as an illustrative case scenario, this paper analyzes how the combined use of extremely fast biased-randomized heuristics and parallel computing allows for 'agile' optimization of routing plans for drones and other autonomous vehicles.
    Thematic Areas: Transportation Interdisciplinar Engenharias iv Engenharias iii Ciências biológicas ii Administração pública e de empresas, ciências contábeis e turismo
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: alejandro.estrada@urv.cat
    Author identifier: 0000-0001-9767-2177
    Record's date: 2024-07-27
    Papper version: info:eu-repo/semantics/publishedVersion
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Transportation Research Procedia. 47 243-250
    APA: Juan AA; Freixes A; Panadero J; Serrat C; Estrada-Moreno A (2020). Routing Drones in Smart Cities: A Biased-Randomized Algorithm for Solving the Team Orienteering Problem in Real Time. Amsterdam: Elsevier
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2020
    Publication Type: Proceedings Paper

  • Keywords:

    Transportation
    Unmanned aerial vehicles
    Team orienteering problem
    Smart cities
    Transportation
    Interdisciplinar
    Engenharias iv
    Engenharias iii
    Ciências biológicas ii
    Administração pública e de empresas, ciências contábeis e turismo

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