为降低疫情防控对物资运输的影响，首先，在综合考虑不同客户类型、服务时间窗与多车型服务车队等因素的前提下，以最小化由物流供应商运输、防疫和违背时间窗惩罚组成的综合运输成本为目标，建立考虑常态化疫情防控的物资运输路径优化模型。然后，设计结合部分匹配交叉与精英保留策略的遗传算法对模型进行求解。最后，分别应用LINGO软件与遗传算法求解具有不同规模与分布的多组Solomon算例，并通过调整输入参数对模型进行敏感度分析。结果表明：所建模型可以在考虑常态化疫情防控因素的前提下，有效优化物资运输路径；所设计的遗传算法具有良好的求解速度与精度；敏感性分析揭示出防疫成本与网络内部系统配车数存在负相关性，决策者可根据实际需求权衡。

In order to reduce the impact of epidemic prevention and control on transportation, a mathematical model was developed to optimize vehicle route considering multi-type customers, time window and multi-type vehicle service fleet. The cost of transportation, epidemic prevention and penalty for violating the time window was minimized by the model. A tangible genetic algorithm (GA) combining partially matched crossover and elitism strategy was designed to find the optimized solution. The designed GA and LINGO were applied to solve the Solomon examples with different scales and distributions. The results showed that the route was efficiently optimized by the developed model considering regular epidemic prevention and control; the designed GA showed good computational efficiency; the sensitivity analyses revealed that the epidemic prevention cost was negatively correlated to the number of vehicles, which should be determined by policy makers.