Combining parallel computing and biased randomization for solving the team orienteering problem in real-time

In smart cities, unmanned aerial vehicles and self-driving vehicles are gaining increased concern. These vehicles might utilize ultra-reliable telecommunication systems, Internet-based technologies, and navigation satellite services to locate their customers and other team vehicles to plan their rou...

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Detalles Bibliográficos
Autores: Panadero Martínez, Javier, Ammouriova, Majsa, Juan, Angel Alejandro, Agustín Martín, Alba, Nogal Macho, Maria, Serrat Piè, Carles|||0000-0002-1504-5354
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/362011
Acceso en línea:https://hdl.handle.net/2117/362011
https://dx.doi.org/10.3390/app112412092
Access Level:acceso abierto
Palabra clave:Operations research
Algorithms
Programming (Mathematics)
Team orienteering problem
Real-life optimization
Parallel computing
Biased randomization
Smart cities
Unmanned aerial vehicles
Investigació operativa
Algorismes
Programació (Matemàtica)
Àrees temàtiques de la UPC::Matemàtiques i estadística
Descripción
Sumario:In smart cities, unmanned aerial vehicles and self-driving vehicles are gaining increased concern. These vehicles might utilize ultra-reliable telecommunication systems, Internet-based technologies, and navigation satellite services to locate their customers and other team vehicles to plan their routes. Furthermore, the team of vehicles should serve their customers by specified due date efficiently. Coordination between the vehicles might be needed to be accomplished in real-time in exceptional cases, such as after a traffic accident or extreme weather conditions. This paper presents the planning of vehicle routes as a team orienteering problem. In addition, an ‘agile’ optimization algorithm is presented to plan these routes for drones and other autonomous vehicles. This algorithm combines an extremely fast biased-randomized heuristic and a parallel computing approach.