Study On Optimization Of Contactless Distribution Path For Emergency Supplies Under Public Health Emergencies

In recent years,due to the frequent occurrence of major public health events,the safety of people’s lives and property has been seriously affected,bringing great challenges to social and economic development.Timely and effective emergency material dispatch is the key method to reduce the negative impact after the outbreak of public health emergencies.Due to the easy contagiousness of viruses,this has put forward new requirements for the traditional material distribution methods.How to complete the task of material distribution without contact and distribute emergency supplies to people in infected areas in a timely and efficient manner is a major challenge for emergency material distribution.Based on the analysis of the existing research results on the distribution and distribution of emergency supplies,the paper firstly starts from the problem of fair distribution of emergency supplies under the situation of oversupply and underdemand,evaluates the demand urgency of demand points by using hierarchical analysis and entropy power method,gets the value of demand urgency of demand points,and distributes supplies according to the proportion of demand urgency of a certain demand point to the sum of demand urgency of all demand points;then classifies Then,the demand points are classified according to multiple constraints,and different distribution methods are used to provide services to the demand points in different regions;finally,based on this,an innovative "truck-unmanned vehicle" mixed mode distribution scheme is proposed to conduct an in-depth study on the distribution of materials in public health emergencies.Due to the shortage of emergency supplies,the supply may exceed the demand at the beginning of a public health emergency.To solve the problem of fairness in the distribution of emergency supplies,this paper proposes a scheme to distribute supplies according to the proportion of demand urgency at the demand point.In order to determine the relative demand urgency of demand points,this paper firstly takes the shortage rate of supplies,the number of population in the infected area,the number of infected population in the infected area,and the number of susceptible population(elderly and children)in the infected area as the demand urgency evaluation indexes;then uses the hierarchical analysis method and SPSSAU software to determine the subjective weights of evaluation indexes,and uses the entropy weight method and Matlab software to determine the objective weights of evaluation indexes;finally The demand urgency score is obtained,and then the demand urgency is derived to obtain the material distribution quantity at each demand point.After completing the distribution of materials,since the speed of trucks is much higher than that of unmanned vehicles,in order to efficiently complete the task of distributing materials at large quantities of demand points,a mixed distribution mode of "trucks carrying unmanned vehicles" is adopted to complete the distribution at demand points far away from the distribution center,and unmanned vehicles independently complete the distribution at demand points nearer to the distribution center.A demand point classification model with the maximum delivery radius of unmanned vehicle,maximum unmanned vehicle loading capacity of truck,maximum truck loading capacity and maximum unmanned vehicle loading capacity as constraints is constructed,and the improved K-means++ algorithm is designed to solve it before and after.After completing the demand point classification,the total distribution time is calculated by phased modeling considering the possible waiting time between trucks and unmanned vehicles in the actual distribution process.The hybrid distribution path optimization model of "truck-unmanned vehicle" with the goal of shortest distribution time is constructed to solve the unmanned vehicle scheduling problem of parallel operation of two distribution modes with limited number of unmanned vehicles,and multiple swarm genetic algorithms are designed to solve the problem before and after improvement.In order to verify the rationality of the model and the effectiveness of the designed algorithm,this paper adopts the method of simulating the dispatching of emergency supplies at the beginning of a public health emergency in a certain region,and analyzes the cases by using the improved K-means++ algorithm,multiple swarm genetic algorithm and the combination of the two algorithms.The improved K-means++ algorithm can effectively avoid the impact of K-value variability on the clustering results under multiple constraints;the improved multi-population genetic algorithm outperforms the traditional multi-population genetic algorithm in terms of iteration speed and optimal solution;the solution obtained by the combination of the two improved algorithms is much better than the other three combination algorithms.The reasonableness and effectiveness of the model and the algorithm are proved by the analysis of arithmetic cases.