A Green Vehicle Routing Problem with Time-Varying Speeds and Joint Distribution

With the rapid growth of urban logistics demand, carbon emissions and the time-varying nature of vehicle speeds have become critical challenges in sustainable transportation planning. This paper addresses a Time-Dependent Green Vehicle Routing Problem (TDGVRP) that integrates time-varying speeds, carbon emissions, and cold chain logistics under a joint distribution framework involving multiple depots and homogeneous refrigerated vehicles. A Mixed-Integer Linear Programming (MILP) model is developed, explicitly considering carbon pricing, refrigeration energy consumption, and speed variations across different time periods. To efficiently solve large-scale instances, a Three-Phase Heuristic (TPH) algorithm is proposed, combining spatiotemporal path construction, local-improvement strategies, and an Adaptive Large Neighborhood Search (ALNS) mechanism. Computational experiments show that the proposed method outperforms traditional Genetic Algorithms (GAs) in both solution quality and computation time, and in some benchmark cases even achieves better results than the commercial solver Gurobi, demonstrating its robustness and scalability. Using real-world traffic speed data, comparative analysis reveals that the joint distribution strategy reduces total logistics costs by 14.40%, carbon emission costs by 23.12%, and fleet size by approximately 25% compared to single-entity distribution. The findings provide a practical and scalable solution framework for sustainable cold chain logistics routing in time-dependent urban road networks.