A Sim-Learnheuristic for the Team Orienteering Problem: Applications to Unmanned Aerial Vehicles

Autor: Mohammad Peyman, Xabier A. Martin, Javier Panadero, Angel A. Juan
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Algorithms, Vol 17, Iss 5, p 200 (2024)
Druh dokumentu: article
ISSN: 17050200
1999-4893
DOI: 10.3390/a17050200
Popis: 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.
Databáze: Directory of Open Access Journals
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