# Assessing the Performance of Deep Learning Algorithms for Newsvendor Problem

arXiv: https://arxiv.org/abs/1706.02899 · HTML: https://mlsanity.com/paper/1706.02899/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2017.

## Built on by (most cited first, 6 of 6)

- [A Survey of Contextual Optimization Methods for Decision Making under Uncertainty](https://mlsanity.com/paper/2306.10374/) (2023)
- [Bilevel Optimization for Feature Selection in the Data-Driven Newsvendor Problem](https://mlsanity.com/paper/2209.05093/) (2022)
- [Integrated Optimization of Predictive and Prescriptive Tasks](https://mlsanity.com/paper/2101.00354/) (2021)
- [The Contextual Appointment Scheduling Problem](https://mlsanity.com/paper/2108.05531/) (2021)
- [Prescribe-then-Select: Adaptive Policy Selection for Contextual Stochastic Optimization](https://mlsanity.com/paper/2509.08194/) (2025)
- [Variable Selection for Feature-Based Newsvendor](https://mlsanity.com/paper/2609.01544/) (2026)

## Abstract (arXiv)

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.