疫情期间社区商超物资配送路径优化研究

Research on the optimization of distribution route for community supermarket materials during the epidemic situation

在抗击新型冠状病毒肺炎的战役中,采取“商超+社区”创新无接触配送模式对疫情影响下的社区进行物资配送,以保障居民生活物资供应。利用SOM神经网络算法与遗传算法求解车辆路径规划中的特例问题——TSP问题,以湖北省黄石港区社区物资配送路径为例,对两种算法的优化结果进行对比,发现遗传算法比SOM神经网络算法拥有更高的求解精度及效率。结果表明:优化后的路径距离为初始路径距离的37.2%,使用遗传算法可更快地搜寻到最佳的物资配送路径,得到较好的物资配送方案。

In the battle against Covid-19,the“supermarket+community”innovative contactless distribution mode has been adopted to deliver materials to communities affected by the epidemic,so as to ensure daily supplies to the residents.A SOM neural network algorithm is used with a genetic algorithm to solve the TSP problem,a special case in vehicle route planning.Taking the community material distribution route in Huangshigang district of Hubei Province as an example,comparing the optimization result of these two algorithms,this paper finds that the genetic algorithm has higher solution accuracy and efficiency than the SOM neural network algorithm.The result shows that the optimized path distance is 37.2%of the initial path distance.The genetic algorithm can find the best material distribution path and get a better material distribution scheme.Then,the influence of important parameters such as population size and iteration number on the material distribution scheme is tested.The epidemic situation is kind of order,and prevention-control means responsibility.The rapid generation of a better distribution route plan can ensure the supply of daily living materials for the community residents affected by the epidemic situation.