Assortment Optimization under Heteroscedastic Data

Subject:Assortment Optimization under Heteroscedastic Data

Guest: Xiaobo Li, Assistant Professor, the National University of Singapore 

Host: Taotao He, Assistant Professor, Antai

Time: Thursday, Oct 28, 2021, 14:00-15:30

Venue: A305, Antai

Abstract:

We study assortment problems under the marginal exponential model (MEM), which is an extension of the multinomial logit (MNL) model that can capture heteroscedasticity in the data. We first show that when the variance of the outside option is the largest, one of the profit-nested assortments is optimal. This result generalizes the well-known result for the MNL assortment optimization problem. Next, we show that the product assortment problem under MEM is NP-hard, but the best profit-nested assortment provides a good approximation to the optimal assortment. Furthermore, we improve existing MEM parameter estimation methods. Our numerical studies show that using MEM to capture choice behavior in assortment optimization leads to competitive results compared to other choice models that are also designed to capture heteroscedasticity.

Bio:

Xiaobo Li is an assistant professor in the Department of Industrial Systems Engineering and Management at the National University of Singapore. He received my Ph.D. in Industrial Engineering from the University of Minnesota in 2018. His research mainly focuses on robust optimization and demand learning, with applications in revenue management, data-driven decision making, and sharing economy.

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