Retracted: Logistics Optimization Strategy Based on Deep Neural Framework

Abstract

We propose a logistics optimization method based on improved graph convolutional networks to address the current problem oflow product delivery rate and untimely product delivery during the peak period of e-commerce activities. Our method can learnexcellent planning strategies from previous data and can give the best logistics strategy in time during the peak logistics period,which improves the product delivery rate and delivery time of logistics and greatly enhances the return on investment. First, weadd a tensor rotation module to the graph convolution layer to better capture the global features of logistics nodes. ,en we addinception structures in the temporal convolution layer to build multiscale temporal convolution filters to obtain temporalinformation of logistics nodes in different time-aware domains and reduce arithmetic power. Finally, we cooperate withe-commerce platforms to adopt logistics data as the experimental database. ,e experimental results show that our method greatlyaccelerates the logistics planning speed, improves the product delivery rate, ensures the timely delivery of products, and increasesthe return on investment