Co-optimization Of Urban Temporary Distribution Station Location And Distribution

In recent years,plagued by the COVID-19 epidemic,residents’ lives have been affected and changed,and they are in a state of normal epidemic prevention and control,and the time of lockdown is unpredictable,and the epidemic infestation is uncertain.The storage and distribution of fresh products play an important role.Emergencies of different nature make the location of distribution points in the city different,resulting in the fixed and long-term distribution points after idle,the old distribution points can not meet the optimal distribution conditions,resulting in a waste of resources.Therefore,in this context,how to quickly and accurately deliver fresh products to demand points has great practical significance.This thesis mainly of emergent public health events as the background,based on the characteristics of fresh product supply and demand,according to the distribution of demand point,consider the total time cost,from the perspective of reducing waste of resources,according to the demand of each point,the use of city square,stadium,exhibition center such as idle in a disaster site,set up temporary distribution station distribution fresh products in order to solve the above problems.Firstly,the distribution of demand points and demand during the special period of epidemic lockdown in urban areas were studied,and the distribution mode of temporary distribution stations in urban areas was analyzed.Secondly,taking the shortest completion time of distribution as the optimization goal,a collaborative optimization model of location selection and distribution was established,and compared with location selection model and path optimization model,highlighting the accuracy and efficiency of the collaborative optimization model.Finally,the main urban area of Handan City is divided into several demand points,and a number of alternative addresses are selected.Combined with the actual data,the immune optimization algorithm is used to solve the model,and the rationality and efficiency of the proposed method and model are verified.Based on the above research results,some decision support is provided to solve the collaborative optimization problem of location selection and distribution.