Reinforcement Learning for Efficient Drone-Assisted Vehicle Routing

Abstract

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.