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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Veröffentlicht in:Applied sciences Jg. 11; H. 24; S. 12092
Hauptverfasser: Panadero, Javier, Ammouriova, Majsa, Juan, Angel A., Agustin, Alba, Nogal, Maria, Serrat, Carles
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.12.2021
Schlagworte:
biased randomization
Decision making
Drones
Genetic algorithms
Heuristic
Internet of Things
Monte Carlo simulation
Optimization algorithms
Orienteering
parallel computing
real-life optimization
Sensors
Smart cities
Surveillance
team orienteering problem
Traveling salesman problem
Unmanned aerial vehicles
Vehicles
ISSN:2076-3417, 2076-3417
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2076-3417
2076-3417
DOI:10.3390/app112412092