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

Javier Panadero, Majsa Ammouriova, Angel A. Juan*, Alba Agustin, Maria Nogal, Carles Serrat

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

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.

Original languageEnglish
Article number12092
Number of pages18
JournalApplied Sciences (Switzerland)
Volume11
Issue number24
DOIs
Publication statusPublished - 2021

Keywords

  • Biased randomization
  • Parallel computing
  • Real-life optimization
  • Smart cities
  • Team orienteering problem
  • Unmanned aerial vehicles

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