讲座题目 | Error propagation in Data-Driven Multi-Period Inventory | ||
主讲人 （单位） | YE Zhisheng | 主持人 （单位） | 丁溢 |
讲座时间 | 2025年5月12日上午10点 | 讲座地点 | 经管楼B201 |
主讲人简介 |
Dr. Ye received a joint B.E. (2008) in Material Science & Engineering, and Economics from Tsinghua University. He received a Ph.D. degree from National University of Singapore. He is currently an Associate Professor and Dean's Chair in the Department of Industrial Systems Engineering & Management at National University of Singapore. His research areas include reliability modeling, industrial statistics and data-driven operations management. | ||
讲座内容摘要 | We study periodic review stochastic inventory control in the data-driven setting, in which the retailer makes ordering decisions based only on historical demand observations without any knowledge of the probability distribution of the demand. We investigate the statistical properties of the data-driven (s, S)-policy obtained by recursively computing the empirical cost-to-go functions. This estimator is inherently challenging to analyze because the recursion induces propagation of the estimation error backward in time. In this work, we establish asymptotic properties of this data-driven policy by fully accounting for the error propagation. First, we rigorously show the consistency of the estimated parameters by filling in some gaps (due to unaccounted error propagation) in the existing studies. On the other hand, empirical process theory cannot be directly applied to show asymptotic normality since the empirical cost-to-go functions for the estimated parameters are not i.i.d. sums, again due to the error propagation. Our main methodological innovation comes from an asymptotic representation for multi-sample U-processes in terms of i.i.d. sums. This representation enables us to apply empirical process theory to derive the influence functions of the estimated parameters and establish joint asymptotic normality. Based on these results, we also propose an entirely data-driven estimator of the optimal expected cost and we derive its asymptotic distribution. | ||
