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1. Afshin Oroojlooyjadid, Lehigh University
2. Lawrence V. Snyder, Lehigh University
3. Martin Takác, Lehigh University

2018

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.

Oroojlooyjadid, Afshin, Lawrence Snyder, and Martin Takáč. “Applying deep learning to the newsvendor problem.” arXiv preprint arXiv:1607.02177 (2016).

• https://arxiv.org/pdf/1607.02177.pdf
• https://www.tandfonline.com/doi/pdf/10.1080/24725854.2019.1632502?needAccess=true
• https://eyun.baidu.com/s/3kWMzwEV

1. 论文是关于什么的？[请提供该论文的简要摘要。]

解决多特征报童（Multi-Feature Newsvendor, MFNV）问题的五种常见方法：

1. Estimate-as-Solution (EAS)
2. Separated Estimation and Optimization (SEO)
3. Empirical Quantile Method
4. Integrating ML in Optimization
5. Linear Machine Learning (LML) Method

2. 这篇论文的长处和短处是什么？[请以以下角度评述：（a）创新（研究问题、建模、方法等）；（b）相关性（研究问题、发现等）；（c）严谨性（适当的方法、分析的正确性等）]

研究问题、建模、方法等

研究问题、发现

适当的方法、分析的正确性等

3.如果有的话，潜在改进的主要地方是什么？[如果这些关键要求和建议能够被适当处理，请重点关注能使文章发表的关键要求和建议。如果你看到不可逾越的障碍，请清楚地描述你的担忧。如果能为编辑和作者提供具体有建设性的意见最好不过了，并在可能的情况下，提出可行的建议。同样，应避免含糊不清和/或含糊不清的批评。]

4.如果有的话，潜在改进的微小地方是什么？[再次，请具体说明。]

5.有没有机会做一项新的研究？