基于分布鲁棒优化的多隔室冷链车辆路径规划模型研究OA

This study investigates the vehicle routing problem for multi-compartment cold chain vehicles transporting refrigerated and frozen goods.First,considering the dual uncertainties of customer demand and environmental temperature,as well as compartment capacity constraints affecting routing decisions and refrigeration energy consumption,a distributionally robust optimization model with fuzzy chance constraints is formulated to minimize total costs.Second,a hybrid distributionally robust optimization method integrating moment information and Wasserstein distance is proposed to construct ambiguity sets for demand and temperature uncertainties.The original model is transformed into computationally tractable integer second-order cone programming and mixed-integer linear programming formulations.Finally,the Beluga Whale optimization algorithm is employed to identify optimal routes.Comparative simulations with other uncertainty-handling methods demonstrate that the distributionally robust optimization approach exhibits stronger robustness,avoids extreme conservatism,and achieves better cost-effectiveness in addressing uncertainty.