Deep Reinforcement Learning for Solving Electric Vehicle Routing Problems with Battery Swapping Station

With the widespread adoption of electric vehicles EVs in logistics and transportation, long charging times and limited range have become significant challenges. Battery swapping offers an efficient station energy replenishment method that can substantially reduce replenishment time. This study investigates the Electric Vehicle Routing Problem with Battery Swapping Station EVRP-BSS . To address this problem, a Deep Reinforcement Learning DRL -based approach is proposed to solve the EVRP-BSS by training an encoder-decoder structured policy network that constructs vehicle routes sequentially. First, the construction of the EVRP-BSS solution is modeled as a Markov Decision Process MDP . Then, to recognize the relationships more effectively, we design a graph convolutional network GCN -based encoder that separately embeds node features and edge features e.g., distance, slope , further fusing them through self-attention to generate global representations for downstream tasks. This approach enhances the node information, ultimately leading to high-quality solutions. During training, we update the model parameters by the multiple starting sampling trajectory method. Experimental results demonstrate that our method outperforms various traditional and DRL-based baselines while showing strong generalization ability.