报告题目：
Choice-based Assortment Optimization with Distributional Ambiguity
报 告 人：加拿大多伦多开云 孙瑜 博士
报告时间：2025年12月31日（周三） 10:00-11:30
报告地点：文管学馆B306
报告摘要：
We consider assortment optimization problems where the retailer needs to choose a set of products to offer to customers. By choosing the assortment, which will affect customers' purchase choice, the retailer maximizes their expected revenue. We study the robust setting in the sense that we do not have exact knowledge of the distribution of customers' utility from each product. Specifically, we introduce two distributionally robust assortment formulations, robust assortment revenue optimization and robust assortment revenue satisficing. While the former uses a pre-specified ambiguity set to characterize the scope of the probability distributions of customers' utilities, the latter uses a target-driven approach to take all probability distributions into account. By using the multinomial logit model as the reference choice model for both formulations, we derive the worst-case distribution, construct worst-case choice model based on the worst-case distribution and provide insights on the effects of distributionally robust setting. We show that the revenue-ordered property still holds in the optimal robust assortments. We also develop efficient methods to find optimal solutions for the cardinality constrained problem. Computational studies demonstrate that our robust approaches can outperform several benchmark approaches such as multinomial logit choice model, robust Markov chain choice model in terms of expected revenues when the data size is small, or there is model misspecification, or the data is censored. Finally, we extend distributionally robust frameworks to the nested logit choice model and the Markov chain choice model which shows the generality of our frameworks.
报告人简介：
Yu Sun is a Postdoctoral Research Fellow in the Rotman School of Management at the University of Toronto. She received her bachelor's degree in industrial engineering from Nanjing University in 2020 and her Ph.D. from the Department of Systems Engineering and Engineering Management at the Chinese University of Hong Kong in 2025. Her research focuses on robust operations management across various contexts, including healthcare and revenue management. Her work was recognized with the Second Prize for the Best Paper Award at the 2024 CSAMSE Conference.
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