# Applying Deep Learning to the Newsvendor Problem

arXiv: https://arxiv.org/abs/1607.02177 · HTML: https://mlsanity.com/paper/1607.02177/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2016.

## Built on by (most cited first, 24 of 24)

- [A Survey of Contextual Optimization Methods for Decision Making under Uncertainty](https://mlsanity.com/paper/2306.10374/) (2023)
- [Deep Inventory Management](https://mlsanity.com/paper/2210.03137/) (2022)
- [Self-Guiding Exploration for Combinatorial Problems](https://mlsanity.com/paper/2405.17950/) (2024)
- [Achieving Efficiency in Black Box Simulation of Distribution Tails with Self-structuring Importance Samplers](https://mlsanity.com/paper/2102.07060/) (2021)
- [Neural Stochastic Dual Dynamic Programming](https://mlsanity.com/paper/2112.00874/) (2021)
- [Learning Optimal Solutions via an LSTM-Optimization Framework](https://mlsanity.com/paper/2207.02937/) (2022)
- [Multi-Agent Reinforcement Learning with Shared Resources for Inventory Management](https://mlsanity.com/paper/2212.07684/) (2022)
- [Bilevel Optimization for Feature Selection in the Data-Driven Newsvendor Problem](https://mlsanity.com/paper/2209.05093/) (2022)
- [A Conformal Approach to Feature-based Newsvendor under Model Misspecification](https://mlsanity.com/paper/2412.13159/) (2024)
- [Efficient End-to-End Learning for Decision-Making: A Meta-Optimization Approach](https://mlsanity.com/paper/2505.11360/) (2025)
- [Proactive Resource Request for Disaster Response: A Deep Learning-based Optimization Model](https://mlsanity.com/paper/2307.16661/) (2023)
- [Smart Surrogate Losses for Contextual Stochastic Linear Optimization with Robust Constraints](https://mlsanity.com/paper/2505.22881/) (2025)
- [Business Metric-Aware Forecasting for Inventory Management](https://mlsanity.com/paper/2308.13118/) (2023)
- [ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention](https://mlsanity.com/paper/2506.11052/) (2025)
- [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)
- [Private Optimal Inventory Policy Learning for Feature-based Newsvendor with Unknown Demand](https://mlsanity.com/paper/2404.15466/) (2024)
- [Learning Decisions Offline from Censored Observations with ε-insensitive Operational Costs](https://mlsanity.com/paper/2408.07305/) (2024)
- [Prescribe-then-Select: Adaptive Policy Selection for Contextual Stochastic Optimization](https://mlsanity.com/paper/2509.08194/) (2025)
- [ORPR: An OR-Guided Pretrain-then-Reinforce Learning Model for Inventory Management](https://mlsanity.com/paper/2512.19001/) (2025)
- [Deep Learning for Perishable Inventory Systems with Human Knowledge](https://mlsanity.com/paper/2601.15589/) (2026)
- [Smooth Learning with Hard Constraints via Legendre-Regularized Policies](https://mlsanity.com/paper/2607.24007/) (2026)
- [Contextual Deconvolution for Variance-Stable Demand Sensing: Kernel-Modulated Operators in Promotional Retail](https://mlsanity.com/paper/2607.25664/) (2026)
- [Variable Selection for Feature-Based Newsvendor](https://mlsanity.com/paper/2609.01544/) (2026)

## Abstract (arXiv)

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.