A Simulation-Optimisation Tool for Routing Drones in Realistic Conditions
[EN] The use of drones for routing and monitoring tasks has grown significantly, with applications such as traffic surveillance and road inspections gaining prominence . These real-world scenarios often involve unpredictable factors like fluctuating service times, which add complexity to traditional...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:dnet:riunet______::1f5656f86b8b6d77cdcccec940e68ab4 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/235160 |
| Access Level: | acceso abierto |
| Palabra clave: | Routing drones Simulation-optimisation Uncertainty |
| Sumario: | [EN] The use of drones for routing and monitoring tasks has grown significantly, with applications such as traffic surveillance and road inspections gaining prominence . These real-world scenarios often involve unpredictable factors like fluctuating service times, which add complexity to traditional routing problems. This paper introduces a simulationoptimisation framework for routing drones under realistic conditions . To efficiently solve this problem, we propose a simheuristic approach that integrates a biased-randomised iterated local search metaheuristic with Monte Carlo simulation. Our computational experiments validate the efficiency, robustness, and speed of the proposed method, providing high-quality solutions to routing challenges in uncertain environments. |
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