Learning Newsvendor Problems With Intertemporal Dependence and Moderate Non-stationarities
研究了当上下文数据存在跨期依赖和非平稳性时，如何为数据驱动的报童问题提供性能保证，对处理真实运营环境中的库存决策有参考价值。
This work provides performance guarantees for solving data-driven contextual newsvendor problems when the contextual data contains intertemporal dependence and non-stationarities. While machine learning tools have observed increasing use in data-driven inventory management problems, most of the existing work assumes that the contextual data are independent and identically distributed (often referred to as i.i.d.). However, such assumptions are often violated in real operational environments where the contextual data are sequentially generated with intertemporal correlations and possible non-stationarities. By accommodating these naturally arising operational environments, our work adopts comparatively more realistic assumptions and develops out-of-sample performance bounds for learning data-driven contextual newsvendor problems.