SILO: Dynamic Assortment Optimization: Fluid Relaxations, Submodularity, and Approximation Algorithms

Abstract

Assortment and inventory decisions lie at the core of supply chain and retail operations. In many settings, these decisions are complicated by customer substitution: when a preferred product is unavailable, customers may switch to other products, and as inventory is depleted over time, the demand faced by the remaining products changes dynamically. This gives rise to the dynamic assortment optimization problem, which is known to be computationally challenging. In this talk, I will introduce the problems for both brick-and-mortar retail and online settings. By leveraging fluid relaxations and submodularity-like structural properties, we develop more efficient approximation algorithms with improved performance guarantees across a range of settings, including different choice models, assortment policies, inventory constraints, and uncertainty in the number of customers.

Bio

Shuo Sun is an Assistant Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin–Madison. She received her Ph.D. in Industrial Engineering and Operations Research from UC Berkeley, where she was advised by Professor Zuo-Jun Max Shen and Professor Rajan Udwani. Prior to that, she earned her B.Eng. in Industrial Engineering from Tsinghua University, China. Her research focuses on developing scalable optimization and machine learning methods for problems in supply chain management, retail, and platform operations, including assortment optimization, inventory management, and pricing.