Title: Assessing the Performance of Deep Learning Algorithms for Newsvendor Problem,
Abstract: In retailer management, the Newsvendor problem has widely attracted attention
as one of basic inventory models. In the traditional approach to solving this
problem, it relies on the probability distribution of the demand. In theory, if
the probability distribution is known, the problem can be considered as fully
solved. However, in any real world scenario, it is almost impossible to even
approximate or estimate a better probability distribution for the demand. In
recent years, researchers start adopting machine learning approach to learn a
demand prediction model by using other feature information. In this paper, we
propose a supervised learning that optimizes the demand quantities for products
based on feature information. We demonstrate that the original Newsvendor loss
function as the training objective outperforms the recently suggested quadratic
loss function. The new algorithm has been assessed on both the synthetic data
and real-world data, demonstrating better performance.