Optimization of Cross-border E-commerce Logistics Distribution Network Based on Genetic Neural Network

Abstract

This paper discusses the optimization of cross-border e-commerce logistics distribution network based on genetic neural network. Based on the logistics preferences of different types of customers (that is, high timeliness or low cost of distribution), the design problem of e-commerce logistics distribution network is modeled as a multi-objective optimization problem. The GNN algorithm is improved to solve the multi-objective optimization problem efficiently. Cross-border electronic commerce logistics is an important part of the international trade process. The logistics mode of cross-border e-commerce shortens the value chain. It accelerates the speed of international logistics, but it also complicates the research of cross-border logistics networks. In the aspect of Cross-border electronic commerce logistics distribution optimization: combining the characteristics of Cross-border electronic commerce logistics distribution, establish an optimization model with cost as the goal. Through standard test examples, it is found that the performance of the algorithm needs to be strengthened. Based on analyzing the limitations of the algorithm, the performance of the algorithm is improved, and the validity of the model and the algorithm for Cross-border electronic commerce logistics distribution optimization is verified by numerical examples and actual case data. To solve this complex and dynamic multi-dimensional objective optimization problem, this paper intends to apply genetic neural network to distribution, which logistics cost, customer satisfaction, logistics time cost and other factors.