考虑不确定能耗和个性化客户需求的电动冷藏车辆路径优化

Route optimization of electric refrigerated vehicle considering energy consumption uncertainty and personalized customer demand

在国家“双碳”战略下,采用电动冷藏车替代传统燃油车,开展城市冷链配送是大势所趋.电动冷藏车配送作业易受诸多不确定因素的影响,导致电能消耗难以精准估算.如何在能耗不确定条件下确保车辆电池续航里程能完成配送任务,并满足客户对产品新鲜度和个性化时间窗的需求,是电动冷藏车辆路径优化面临的挑战.本文借助车辆纵向动力学模型和微分方程及鲁棒优化理论分析了电动冷藏车配送作业中的能耗影响因素,如电池续航能力限制、能耗不确定性、产品新鲜度和客户软时间窗约束等,及其产生的各项成本,构建了以总成本最小为目标的两阶段路径优化模型,并设计混合遗传粒子群算法对模型求解,最后以案例验证了模型和算法的有效性.

To achieve China's carbon peak and neutrality goals,using electric refrigerated vehicles to replace traditional fuel vehicles to carry out urban cold chain distribution becomes a general trend.However,the distribution operation of electric refrigerated vehicles is vulnerable to many uncertain factors,which makes it difficult to accurately estimate the power consumption.How to ensure that the battery life of the vehicle can com‐plete the distribution task and effectively meet customer's demand for product freshness and personalized time window under the condition of uncertain energy consumption is a challenge for the route optimization of electric refrigerated vehicles.In this study,a two-stage vehicle path optimization model with the goal of minimizing the total cost is constructed by comprehensively considering the battery life limit,energy consumption uncertainty,product freshness and customer soft time window constraints.Specifically,based on the force analysis of the driving process of electric refrigerated vehicles using the vehicle longitudinal dynamics model,the driving energy consumption model and auxiliary energy consumption model are put forward to get the total energy consumption model of vehicle distribution operation.Furthermore,the robust optimization method is used to deal with the uncertain parameters in the model.A hybrid genetic particle swarm optimization algorithm is designed to solve the model.Finally,an actual case is used to verify the effectiveness of the model and algorithm.