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

  • Dades identificatives

    Identificador: imarina:6311372
    Autors:
    Juan AAFreixes APanadero JSerrat CEstrada-Moreno A
    Resum:
    © 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.
  • Altres:

    Autor segons l'article: Juan AA; Freixes A; Panadero J; Serrat C; Estrada-Moreno A
    Departament: Enginyeria Informàtica i Matemàtiques
    e-ISSN: 2352-1465
    Autor/s de la URV: Estrada Moreno, Alejandro
    Paraules clau: Unmanned aerial vehicles Team orienteering problem Smart cities
    Resum: © 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.
    Àrees temàtiques: Transportation Interdisciplinar Engenharias iv Engenharias iii Ciências biológicas ii Administração pública e de empresas, ciências contábeis e turismo
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'autor: alejandro.estrada@urv.cat
    Identificador de l'autor: 0000-0001-9767-2177
    Data d'alta del registre: 2024-07-27
    Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
    Enllaç font original: https://www.sciencedirect.com/science/article/pii/S2352146520302908
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: Transportation Research Procedia. 47 243-250
    Referència de l'ítem segons les normes 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
    DOI de l'article: 10.1016/j.trpro.2020.03.095
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2020
    Tipus de publicació: Proceedings Paper
  • Paraules clau:

    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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