Research On Distribution Optimization Under The Community Group Purchase Mode Of Fresh Agriculture Products

With the rapid development of the digital economy and mobile convenient payment,the outbreak of the COVID-19 has spawned a need for non-contact transactions,which has ushered in explosive growth of community group buying of fresh agricultural products on the basis of the original vigorous development.With the rapid growth of supply methods and supply channels of community group purchase,the types of agricultural products are also increasing,resulting in the rising operating costs of businesses.Due to the high loss rate and high distribution cost of fresh agricultural products of different categories on the way to the consumer end,it is necessary to build a targeted distribution service management system according to the differentiated needs of different groups of fresh agricultural products,improve the quality and efficiency of distribution work and avoid affecting product quality.When the community group purchase is promoted and occupied in a certain underground market,the cognitive analysis of l ocal consumers’ product preferences and consumption preferences is relatively lacking,which can not fully guarantee the delivery efficiency of the last mile.This article focuses on the optimization of delivery routes by analyzing the current situation of community group buying of fresh agricultural products.By constructing a mathematical model that minimizes the total cost of a group buying platform enterprise,including fixed costs,transportation costs,energy consumption costs,satisfaction penalty costs,and group leader self pickup operating costs,we innovatively introduce Tent chaotic mapping to improve the ant colony algorithm,solve the last delivery path,and shorten delivery time to improve customer satisfaction.The research shows that the improved genetic algorithm proposed in this paper has a better effect in reducing the total cost of commodity distribution and improving customer satisfaction.The model parameters and algorithm parameters are set according to the case data,and the improved ant colony algorithm is used to solve the case data to obtain the optimal distribution path.In order to eliminate the impact of the case data,50 and 100 random data are also set to prove the effectiveness of the model.Finally,the improved ant colony algorithm and the ant colony algorithm are compared to prove the effectiveness of the algorithm proposed in this paper,which can be applied to the actual scene of platform commodity distribution.