Reinforcement Learning for Efficient Drone-Assisted Vehicle Routing
Bogyrbayeva A. Dauletbayev B. Meraliyev M.
February 2025Multidisciplinary Digital Publishing Institute (MDPI)
Applied Sciences (Switzerland)
2025#15Issue 4
Many exact algorithms, heuristics, and metaheuristics have been proposed to solve the Vehicle Routing Problem with Drones, which involves using a fleet of trucks and drones to fulfil customer orders in last-mile delivery. In this study, the problem is formulated using the Markov Decision Process, and a Reinforcement Learning (RL) based solution is proposed. The proposed RL model is based on an attention-encoder and a recurrent neural network-decoder architecture. This approach enhances coordination by determining which vehicles should visit specific customers and where vehicles can rendezvous, effectively leveraging drones and reducing the overall completion time. The RL model has demonstrated competitive performance compared to benchmark algorithms through extensive experiments.
neural combinatorial optimization , reinforcement learning , vehicle routing
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Department of Computer Science, SDU University, Kaskelen, 040900, Kazakhstan
Department of Computer Science
10 лет помогаем публиковать статьи Международный издатель
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