A Logistics Network Distribution Algorithm Based on Deep Learning
Panfeng Xu, Jiaqi Miao, Shengsheng Zhou, Hongyang Liu, Youming Wang · 2019
With the rapid development of logistics industry, the position of outlets in logistics system is becoming more and more important. In the logistics system, the planning and layout are extremely important. Correct planning of the location can improve the efficiency of the resource effectively, which plays a crucial role in realizing the efficient operation of the logistics system. Limited by China's vast territory, large population, unbalanced regional economic development and other practical conditions, it is difficult to adapt the complex situation with traditional single-layer one-element logistics outlets model and single-layer multi-element logistics network model. There are certain limitations and deficiencies with traditional models to utilize logistics outlets resource. In this paper, the logistics problem is comprehensively sorted out and studied. Meanwhile, combining with the current situation of outlets layout, a multilayer multi-element logistics outlets layout model is proposed with the goal of minimizing transportation cost. Eventually, the location selection and logistics scale of the transfer center and basic outlets are determined by analyzing the layout condition with network model. In this paper, the data is trained by convolutional neural network . The logistics outlets layout problem is analogized with the minimum value calculation problem. After the comparison, the multi-layer multi-element logistics outlets model proposed in this paper can realize the distribution function with the lowest logistics cost.