Applying Deep Learning to the Newsvendor Problem
This addresses inventory optimization for businesses by improving order decisions in uncertain demand scenarios, but it is incremental as it applies existing deep learning techniques to a classic problem.
The paper tackles the newsvendor problem in inventory management by proposing a deep learning algorithm that integrates forecasting and optimization without requiring known demand distributions, and numerical experiments show it outperforms other approaches, especially for high-volatility demands.
The newsvendor problem is one of the most basic and widely applied inventory models. There are numerous extensions of this problem. If the 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, and does not require 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 machine learning approaches, especially for demands with high volatility. Finally, in order to show how this approach can be used for other inventory optimization problems, we provide an extension for (r,Q) policies.