题　目：Distributionally Robust Newsvendor under Stochastic Dominance with a Feature-Based Application

嘉　宾：李晓波，助理教授，新加坡国立大学

主持人：孙海龙 助理教授 tyc1286太阳成集团

时　间：2024年7月30日（周二）10:00-11:30am

地　点：安泰楼A511室

内容简介：

In this paper, we study the newsvendor problem under some distributional ambiguity sets and explore their relations. Additionally, we explore the benefits of implementing this robust solution in the feature-based newsvendor problem. We propose a new type of discrepancy-based ambiguity set, JW ambiguity set, and analyze it within the framework of first-order stochastic dominance. We show that the DRO problem with this ambiguity set admits a closed-form solution for the newsvendor loss. This result also implies that the newsvendor problem under the well-known infinity-Wasserstein ambiguity set and Lévy ball ambiguity set admit closed-form inventory levels as a byproduct. In the application of feature-based newsvendor, we adopt general kernel methods to estimate the conditional demand distribution and apply our proposed DRO solutions to account for the estimation error. The closed-form solutions enable an efficient computation of optimal inventory levels. In addition, we explore the property of optimal robust inventory levels with respect to the non-robust version via concepts of perceived critical ratio and mean repulsion. The results of numerical experiments and the case study indicate that the proposed model outperforms other state-of-the-art approaches, particularly in environments where demand is influenced by covariates and difficult to estimate.

演讲人简介：

Xiaobo Li is an assistant professor in the Department of Industrial Systems Engineering and Management at the National University of Singapore. He received his Ph.D. in Industrial Engineering from the University of Minnesota in 2018. His research mainly focuses on robust optimization, discrete choice modelling and dynamic programming, with applications in revenue management, data-driven decision making and supply chain management. His team has won the 2021 MSOM Data-Driven Research Challenge.

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