Lightweight Graph Capsule Network for Real-Time Dynamic Vehicle Routing

Dynamic Vehicle Route Planning (DVRP) severely challenges logistics system optimization as it must respond to dynamic events like unexpected orders and traffic changes in real time. Traditional methods suffer from high computational overhead and poor adaptability to dynamic environments, while existing deep learning methods struggle to balance solution quality and computational efficiency. To address this, this paper proposes the lightweight graph capsule network (LGCN) algorithm. It encodes dynamic path states into spatio-temporal graph structures, uses a hierarchical capsule mechanism instead of traditional graph convolution, and applies lightweight processing to the hierarchical capsule network. This effectively resolves the conflict between fast response to dynamic changes and high-quality computational results. The method's effectiveness is evaluated by comparison with transformer-based algorithms, OR-Tool algorithms, and Clarke Wright's algorithm. Experiments show that LGCN not only significantly improves real-time dynamic response efficiency but also performs excellently in multiple key metrics such as average routing cost and average vehicles.