A Deep Reinforcement Learning Algorithm for Dynamic EV Routing Problem*

This is the authors' abstract. We don't add key points for this paper.

In this paper, the problem of emergency power supply for electric vehicles in road traffic networks after earthquake disasters is studied. In emergency response, it is important for evs to reach the target shelter as quickly as possible while considering the path dynamic changes caused by secondary disasters. Therefore, in order to solve this problem, this paper classifies it as the Dynamic Electric Vehicle Routing Problem with Time Windows (DEVRPTW), formulates it as a mixed integer linear programming model, and uses the Deep Reinforcement Learning (DRL) algorithm to solve it. The DRL algorithm adopted a dual-end merging Attention (DEMA) mechanism based on Multi-head Attention (MHA) modification. The DEMA model is subsequently trained using a reinforcement algorithm with rollout baselines. Finally, the MHA model, DEMA model and different training algorithms were compared and analyzed by using randomly generated geographic information data. The simulation results show that DEMA performs well when combined with an enhancement algorithm with rollout baseline.