Based on Deep Learning Methods End-To-End Multi-Cycle Replenishment Strategy Research - Wang, Ziheng (International Journal of Computer Science and Information Technology, 2024)
Based on the historical sales data and inventory data of a large retailer, this paper aims to output optimal replenishment decisions from a data-driven perspective, in order to reduce inventory-related costs. The main research body of this paper is the establishment of an end-to-end multi-cycle replenishment model, and a mathematical model of multi-cycle replenishment is constructed after a series of preprocessing of historical datasets. At the same time, an end-to-end multi-cycle replenishment deep learning model is built based on the mathematical model of multi-cycle replenishment, and the end-to-end label construction is carried out by using the preprocessed data set, so as to train the end-to-end multi-cycle replenishment deep learning model, and then compare it with other models.