Technical Note—Data-Driven Newsvendor Problem: Performance of the Sample Average Approximation
研究了数据驱动报童问题中样本均值逼近方法在时间增长时的性能，发现其能达到最优，并分析了需求分布局部平坦性对问题复杂度的影响。
Sample Average Approximation in Data-Driven Newsvendor In the data-driven newsvendor problem, the manager makes sequential inventory decisions while learning the unknown demand distribution based on past demand samples. How does the widely used sample average approximation approach perform in this problem? In “Technical Note—Data-Driven Newsvendor Problem: Performance of the Sample Average Approximation,” Lin, Huh, Krishnan, and Uichanco analyze the performance of the sample average approximation as the time horizon grows, which turns out to be the best possible. The authors also examine how the local flatness of the demand distribution around the optimal order quantity affects the complexity of the problem. They show that the sample average approximation has the best achievable performance in terms of not only the time horizon, but also the local flatness of the demand distribution.