Assessing the Performance of Deep Learning Algorithms for Newsvendor Problem
This work addresses inventory optimization for retailers, but it is incremental as it focuses on improving the loss function within an existing machine learning framework.
The paper tackled the Newsvendor problem in retail inventory management by proposing a supervised learning approach that uses the original Newsvendor loss function as the training objective, demonstrating better performance on synthetic and real-world data compared to a recently suggested quadratic loss function.
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.