Research On Community Group Purchase Terminal Delivery Path Planning Considering Multiple Vehicle Selection

The community group buying model has achieved further development with the help of the COVID-19.However,behind the rapid growth and stable demand,high consumption,high logistics costs and other issues are also constantly bothering community group buying enterprises.At the same time,the country is gradually promoting electric logistics vehicles to replace traditional fuel logistics vehicles in distribution tasks.However,due to the limitations of initial development,electric logistics vehicles still have problems such as short range.How to reduce the impact of the existing shortcomings of electric logistics vehicles on terminal distribution,effectively leverage the advantages of electric logistics vehicles,and improve the distribution quality of community group buying has become an urgent problem to be solved.This study will focus on the end delivery stage of community group buying,focusing on optimizing the delivery path of electric logistics vehicles,improving delivery efficiency,and exploring the cost optimization,efficiency improvement,and model comparison of fuel logistics vehicles and electric logistics vehicles in the end delivery stage of community group buying.Firstly,this study starts with the research on the end delivery of community group buying and the problem of vehicle path planning.It analyzes the end delivery situation of community group buying mode from the perspectives of distribution characteristics,vehicle usage,and cost.It fully considers the cost composition,timeliness,and carbon emissions performance of different types of logistics vehicles in the end delivery process of community group buying,and plans the delivery path of the end stage of community group buying,Build a multi-objective optimization community group buying terminal path planning model.Secondly,an improved genetic algorithm is designed,and the traditional genetic algorithm is improved by using hill climbing and adaptive genetic operation.On the basis of retaining the global optimization ability of the genetic algorithm,the local optimization ability and convergence speed of the genetic algorithm are emphatically improved,and the model is solved.Finally,the service data of Meituan Optimization in a certain area of Tianjin was selected as an example to establish a regional distribution network to improve the simulation content,and it was proved that the improved genetic algorithm can better solve the model in this study.After comparing the optimal operating results of electric logistics vehicles using charging and battery swapping modes with fuel logistics vehicles,it is believed that the optimized battery swapping logistics vehicle can achieve comparable operating economy to fuel logistics vehicles,and the future development prospects of battery swapping logistics vehicles will be broader.