Attention-Enhanced Deep Reinforcement Learning for Electric Vehicle Routing Optimization

Recently, the electric vehicle routing problem (EVRP) has emerged as a significant challenge in the transportation industry. Unlike traditional vehicle routing problems (VRPs) that focus primarily on route optimization for customers, the EVRP must also consider electricity constraints and charging decisions. Although deep reinforcement learning (DRL), particularly with attention-based Transformer architecture, has achieved success in VRPs, it faces significant difficulties when directly applied to the EVRP due to the additional multifaceted constraints and node types. To address these challenges, this paper proposes an attention-enhanced DRL method specifically designed for the EVRP. First, a heterogeneous attention mechanism is developed in the encoder to capture the intrinsic access relationships between customers and charging stations. Then, an embedding enhancement module is integrated to enrich node embeddings by incorporating adjacent distance and solution structure information through a global-local attention mechanism. Finally, a problem-size sensor is incorporated within the decoder to quantify route planning progress, thereby enhancing adaptability to the EVRP constraints. Empirical results demonstrate that the proposed method not only achieves higher efficiency compared to heuristic methods, but also significantly outperforms representative DRL-based approaches in terms of solution quality.