A Sim-Learnheuristic for the Team Orienteering Problem : Applications to Unmanned Aerial Vehicles
Peyman, Mohammad (Universitat Politècnica de València)
Martin, Xabier A. (Universitat Politècnica de València)
Panadero, Javier (Universitat Autònoma de Barcelona. Departament d'Arquitectura de Computadors i Sistemes Operatius)
Juan, Ángel A. (Universitat Politècnica de València)
Universitat Autònoma de Barcelona. Departament d'Arquitectura de Computadors i Sistemes Operatius

Data:	2024
Resum:	In this paper, we introduce a novel sim-learnheuristic method designed to address the team orienteering problem (TOP) with a particular focus on its application in the context of unmanned aerial vehicles (UAVs). Unlike most prior research, which primarily focuses on the deterministic and stochastic versions of the TOP, our approach considers a hybrid scenario, which combines deterministic, stochastic, and dynamic characteristics. The TOP involves visiting a set of customers using a team of vehicles to maximize the total collected reward. However, this hybrid version becomes notably complex due to the presence of uncertain travel times with dynamically changing factors. Some travel times are stochastic, while others are subject to dynamic factors such as weather conditions and traffic congestion. Our novel approach combines a savings-based heuristic algorithm, Monte Carlo simulations, and a multiple regression model. This integration incorporates the stochastic and dynamic nature of travel times, considering various dynamic conditions, and generates high-quality solutions in short computational times for the presented problem.
Ajuts:	Agencia Estatal de Investigación PRE2020-091842 Agencia Estatal de Investigación PID2022-138860NB-I00 Agencia Estatal de Investigación RED2022-134703-T European Commission 101092612
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Biased randomization ; Learnheuristic ; Simheuristic ; Team orienteering problem ; SDG 9 - Industry, Innovation, and Infrastructure
Publicat a:	Algorithms, Vol. 17, Issue 5 (May 2024) , art. 200, ISSN 1999-4893

DOI: 10.3390/a17050200

19 p, 729.4 KB

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