Research On Optimization Of Urban Distribution Network Of G Chain Convenience Store Enterprises Based On Modern Logistics

The modern logistics distribution industry has developed rapidly under the promotion of new retail and Online shopping.In cities,chain convenience stores have become an important business and service enterprise.The distribution of chain convenience store enterprises has the characteristics of multiple frequencies,small batches,complex varieties,and high efficiency,which are related to the better development of chain convenience store enterprises.This paper studies the optimization of the distribution network of chain convenience store enterprises in cities.The optimization of the distribution network of G chain convenience store enterprises is taken as the research object,and a G chain convenience store enterprise distribution network optimization model based on efficiency maximization is constructed,The Simulated annealing improved genetic algorithm multi-objective function solving method is studied,which lays a theoretical foundation for the optimal distribution network scheme of G chain convenience stores.This paper combines the operational characteristics,logistics and distribution characteristics,and supply chain system of chain convenience store enterprises in China to explore the existing problems,analyze the urban distribution network structure of chain convenience stores,and summarize different types of distribution network structures.Starting from the development of urban chain convenience store enterprises and the problems in their distribution networks,this paper analyzes the optimization model of the urban distribution network of G chain convenience store enterprises from the perspective of modern logistics.In order to optimize the distribution network of convenience stores in X city and surrounding areas,the optimization needs and goals to be achieved are analyzed,and the maximum benefit is considered as the distribution goal of G chain convenience store,Construct multi-objective function optimization models with the goals of minimizing delivery cost and delivery time,and analyze the constraints based on actual delivery situations.In order to solve the local convergence and prematurity problems of the genetic algorithm,and improve the search ability of the global optimal solution of the algorithm,the Simulated annealing algorithm is used to improve the genetic algorithm,and the improved genetic algorithm is applied to the solution of the distribution network optimization model of G chain convenience store enterprises to obtain the optimal distribution scheme.Based on the actual operation of chain convenience stores in cities,the collected data was analyzed and processed,and the specific methods and ideas for case data analysis were elaborated.The comparison of the operation results of the genetic algorithm and the Simulated annealing improved genetic algorithm on the distribution and transportation cost verifies the effectiveness of the Simulated annealing improved genetic algorithm.The Simulated annealing improved genetic algorithm(SAGA)is applied to the case optimization,and the distribution time and traffic environment of G chain convenience stores are comprehensively considered.The optimization results of the distribution network path and time are obtained,which has obvious advantages in cost.Based on the research on the urban distribution network optimization of G chain convenience store enterprises based on modern logistics,a distribution network optimization model and multi-objective function solving algorithm were designed.Based on the obtained calculation results and distribution optimization network,reference was provided for the urban distribution management of G chain convenience store enterprises in X city,which has certain theoretical value and practical significance.