Summary of Deep Generative Demand Learning For Newsvendor and Pricing, by Shijin Gong et al.
Deep Generative Demand Learning for Newsvendor and Pricing
by Shijin Gong, Huihang Liu, Xinyu Zhang
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to address data-driven inventory and pricing decisions in the feature-based newsvendor problem. The authors leverage conditional deep generative models (cDGMs) to learn the demand distribution and generate probabilistic demand forecasts conditioned on price and features. This allows for accurate profit estimation and supports the design of algorithms for optimizing inventory and jointly determining optimal pricing and inventory levels. The approach provides theoretical guarantees, including consistency of profit estimation and convergence to the optimal solution. Extensive simulations demonstrate the effectiveness of the method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses special models called “conditional deep generative models” to help companies make good decisions about how much stuff to keep in stock and what price to sell it for. The model looks at things like prices and other features that might affect how many people want to buy something. It then uses this information to predict how many people will actually buy the thing, which helps the company decide how much to stock and what price to set. The model is tested on some examples and works well. This new way of thinking about making business decisions could be useful for other companies too. |
