This paper took the electric vehicle routing problem(EVRP) with constraints of time, load and battery capacity as the research object, it considered its recharging need in transit, constructed a mathematic model aiming at minimizing the total route length, and proposed an algorithm RL-EVRP based on reinforcement learning. The algorithm generated instances sampled from a given distribution, and trained a model by applying a policy gradient method while keeping the route feasible. The trained model could solve other instances from similar distribution without the need to re-train. Simulation results show that the RL-EVRP can get shorter total route length and less number of vehicles and that the reinforcement learning can be applied to complicated combinatorial optimization problem successfully.

[2020-11-05] Printed Article /doc/2019.07.0260/v2