Name: DEM Lunch Seminar with Güzin Bayraksan (Ohio State University)
Start: 2026-05-20T13:00:00+02:00
End: 2026-05-20T14:00:00+02:00
Location: Weicker Building

Contextual Distributional Robust Optimization under Decision-Dependent Uncertainty: A Residuals-Based Approach

Abstract

Contextual Stochastic Optimization (CSO) has emerged as a way model and solve real-world decision-making problems by integrating Machine Learning (ML) with optimization under uncertainty. Many CSO models consider contextual (or side) information that is separate from the decisions. However, in many real-world applications, the decisions of the optimization model significantly affect the uncertain parameters, hence becoming contexts themselves. For example, pricing decisions impact the uncertain demand of a product.

In this talk, we first introduce a methodology for modeling decision‑dependent uncertainty using a residuals-based stochastic optimization approach. We use various ML methods to learn how decisions shape uncertainty and then build a distribution (and thereby ambiguity set) around these predictions using empirical residuals. We form a Distributionally Robust Optimization (DRO) model using the Wasserstein distance, and investigate its theoretical guarantees, including asymptotic optimality and finite sample results. The resulting model is computationally challenging. Therefore, we devise a specialized Benders decomposition algorithm with nonlinear cutting planes to solve the resulting model. We demonstrate the effectiveness of our proposed approach on a pricing and shipment planning problem.

About the speaker

Güzin Bayraksan is a Professor and Associate Chair for Research in the Integrated Systems Engineering Department and an affiliated faculty member of the Sustainability Institute and the Translational Data Analytics Institute at the Ohio State University. Her research interests are in optimization under uncertainty, particularly stochastic and distributionally robust optimization, using data-driven, contextual, and Monte Carlo simulation-based methods. She applies these models and methods to solve problems of critical societal interest in water, energy, and transportation systems. Her papers have appeared in top journals such as Mathematical Programming, SIAM Journal on Optimization and Operations Research, and her research has been supported by multiple grants from the National Science Foundation (NSF) and the Department of Energy (DoE). She is the recipient of INFORMS ENRE Best Publication Award in Environment and Sustainability, Lumley Research Award (OSU), NSF CAREER award, Five Star Faculty Award (UA), and the INFORMS Best Case Study award. She served as the Chair of Stochastic Programming Society (SPS) (2019-2023), the Vice Chair of Optimization under Uncertainty of INFORMS Optimization Society, and the President of the INFORMS Forum on Women in Operations Research and Management Science.