【摘要】 针对考虑碳排放约束的动态车辆路径问题，运用周期性更新的动态响应策略考虑客户增加的动态事件，建立以总成本最小为目标兼顾低碳与动态需求的车辆路径优化模型。提出一种改进的灰狼优化算法，通过Logistic-Tent混沌映射与准反向学习初始化种群，并对收敛因子进行非线性调整，引入基于步长欧式距离的比例权重，最后进行鹈鹕优化算法的融合演化。实验表明，改进算法收敛快、目标函数值低，总成本减少26.53%，满载率增加13.49%，周期性更新策略使总成本减少14.16%，满载率增加14.95%。实验结果验证了算法和策略的有效性，为物流企业配送方案的制定提供了理论参考。更多还原

【Abstract】 Aiming at the dynamic vehicle routing problem considering carbon emission constraints, the dynamic response strategy of periodic update is used to consider the increasing dynamic events of customers,and a vehicle routing optimization model with minimum total cost as the goal and considering both low carbon and dynamic demand is established. At the same time, an improved grey wolf optimization algorithm is proposed, the population is initialized by Logistic-Tent chaotic mapping and quasi-reverse learning, and the convergence factor is adjusted nonlinearly. The proportional weight based on step Euclidean distance is introduced, and finally the fusion evolution of pelican optimization algorithm is carried out.Experiments show that the improved algorithm has fast convergence and low objective function value.The total cost is reduced by 26.53 %, and the full load rate is increased by 13.49%. The periodic update strategy reduces the total cost by 14.16 %,and the full load rate is increased by 14.95 %.The experimental results verify the effectiveness of the algorithm and strategy, and provide a theoretical reference for the formulation of logistics enterprise distribution plan.更多还原