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

  • Datos identificativos

    Identificador: imarina:6311372
    Autores:
    Juan AAFreixes APanadero JSerrat CEstrada-Moreno A
    Resumen:
    © 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.
  • Otros:

    Autor según el artículo: Juan AA; Freixes A; Panadero J; Serrat C; Estrada-Moreno A
    Departamento: Enginyeria Informàtica i Matemàtiques
    e-ISSN: 2352-1465
    Autor/es de la URV: Estrada Moreno, Alejandro
    Palabras clave: Unmanned aerial vehicles Team orienteering problem Smart cities
    Resumen: © 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.
    Áreas temáticas: Transportation Interdisciplinar Engenharias iv Engenharias iii Ciências biológicas ii Administração pública e de empresas, ciências contábeis e turismo
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: alejandro.estrada@urv.cat
    Identificador del autor: 0000-0001-9767-2177
    Fecha de alta del registro: 2024-07-27
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://www.sciencedirect.com/science/article/pii/S2352146520302908
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Transportation Research Procedia. 47 243-250
    Referencia 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 del artículo: 10.1016/j.trpro.2020.03.095
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2020
    Tipo de publicación: Proceedings Paper
  • Palabras clave:

    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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