College of Business Administration and Accountancy, De La Salle University-Dasmariñas, Cavite 4115, Philippines.
Comput Intell Neurosci. 2022 Jun 6;2022:8367155. doi: 10.1155/2022/8367155. eCollection 2022.
We propose a logistics optimization method based on improved graph convolutional networks to address the current problem of low product delivery rate and untimely product delivery during the peak period of e-commerce activities. Our method can learn excellent 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, we add a tensor rotation module to the graph convolution layer to better capture the global features of logistics nodes. Then we add inception structures in the temporal convolution layer to build multiscale temporal convolution filters to obtain temporal information of logistics nodes in different time-aware domains and reduce arithmetic power. Finally, we cooperate with e-commerce platforms to adopt logistics data as the experimental database. The experimental results show that our method greatly accelerates the logistics planning speed, improves the product delivery rate, ensures the timely delivery of products, and increases the return on investment.
我们提出了一种基于改进图卷积网络的物流优化方法，以解决电子商务活动高峰期产品配送率低和产品配送不及时的问题。我们的方法可以从以往的数据中学习优秀的规划策略，并在物流高峰期及时给出最佳物流策略，提高物流的产品配送率和配送时间，大大提高投资回报率。首先，我们在图卷积层中添加张量旋转模块，以更好地捕获物流节点的全局特征。然后，我们在时间卷积层中添加 inception 结构，构建多尺度时间卷积滤波器，以获取不同时间感知域中物流节点的时间信息，并减少计算量。最后，我们与电子商务平台合作，采用物流数据作为实验数据库。实验结果表明，我们的方法大大加快了物流规划速度，提高了产品配送率，保证了产品的及时交付，提高了投资回报率。
