Applying Deep Learning to the Newsvendor Problem

The newsvendor problem is one of the most basic and widely applied inventory models. There are numerous extensions of this problem. One important extension is the multi-item newsvendor problem, in which the demand of each item may be correlated with that of other items. If the joint probability distribution of the demand is known, the problem can be solved analytically. However, approximating the probability distribution is not easy and is prone to error; therefore, the resulting solution to the newsvendor problem may be not optimal. To address this issue, we propose an algorithm based on deep learning that optimizes the order quantities for all products based on features of the demand data. Our algorithm integrates the forecasting and inventory-optimization steps, rather than solving them separately as is typically done. The algorithm does not require the knowledge of the probability distributions of the demand. Numerical experiments on real-world data suggest that our algorithm outperforms other approaches, including data-driven and SVM approaches, especially for demands with high volatility.