报告题目 | A RobustData-driven Newsvendor Problem with Non-parametric Information | |
报告人（单位） | LiangXu | |
主持人（单位） | 薛巍立（东南大学） | 李四杰（东南大学） |
时间地点 | 时间：2020年8月21日（周五 ） 上午10点 腾讯会议ID：900526588（密码：200821） | |
报告内容摘要 | In this article, a data-driven newsvendor problem is considered, in which only historical data on demand is available. In the existing data-driven newsvendor problem literature, historical data is adopted to estimate the density curve and in tuns the optimal order quantity under estimated density curve (empirical data-driven approach). Although the estimated density curve has convergence property when the sample size is large enough, the performance for this approach under limited data is unknown. With this concern in mind, we propose a robust approach based on non-parametric information for the data-driven newsvendor problem. Based on partial nonparametric information such as support, monotonicity, convexity, we develop three methods to partition the support for single-period setting. Under each partitioning method, we construct a distribution ambiguity set, build a protection curve to approximate the real distribution, and base on it to obtain closed-formexpressions for the order quantities that maximize the worst-case expected profit for the newsvendor problem. Taking the sample size and confidence level into consideration, weexplore an adaptive partitioning method for multi-period setting based on the three partitioning methods developed in single-period setting. Moreover, we compare the non-parametric approach to existing parametric approach in yielding the worst-case performance in profit generation under mass function and discrete choice model. | |
报告人简介：Dr. Liang Xu is currently a professor at the Big Data Research Institute of School of Business Administration, The Southwestern University of Finance and Economics (SWUFE). He received a bachelor’s degree in mathematics from Beijing Normal University and a master’s degree in management science in Sun Yat-Sen University. He received a PhD in Logistics and Maritime Studies from the Hong Kong Polytechnic University. He serves as the director of MBA Center in SWUFE and the assistant director of Big Data Research Institute in SWUFE. He is interested in the research areas of robust optimization, stochastic models in finance, intelligent investment, and intelligent transportation. His papers have been published in journals such as TRB, EJOR, NRL, ORL, IJPR, JORS, Omega and so on. His team is now working on development of intelligent investment strategies and system based on big data and optimization technique. He is currently a consultor for Huaxi securities on the risk control in intelligent investment and FoF management. His team has won the second prize for innovation awarded by Banking and Insurance Regulatory Commission. | ||
