11/20/25 | 4:15pm | E51-145
Timothy Chan
Associate Vice-President and Vice-Provost, Strategic Initiatives; Professor, Department of Mechanical and Industrial Engineering
University of Toronto
Abstract: Inverse optimization is increasingly used to estimate unknown parameters in an optimization model based on decision data. However, when such “point estimates” are used prescribe downstream decisions, the resulting decisions may be of low-quality and misaligned with human intuition, and thus less likely to be adopted. To tackle this challenge, we propose a novel decision recommendation pipeline that learns an uncertainty set for the unknown parameters and then solves a robust optimization model to prescribe new decisions. We show that the suggested decisions can achieve bounded optimality gaps, as evaluated using both the ground-truth parameters and human perceptions. Our method demonstrates strong empirical performance compared to the standard inverse optimization pipeline. Finally, we perform a case study where we apply this new pipeline to provide delivery route recommendations in Toronto, Canada. Our approach achieves a significantly higher delivery path adherence rate than current industry practices without compromising service quality. Moreover, our method provides a better trade-off between absolute and perceived decision quality than baselines under various realistic scenarios, including cases with model mis-specification and data scarcity.
Bio: Timothy Chan is the Associate Vice-President and Vice-Provost, Strategic Initiatives, and a Professor in the department of Mechanical and Industrial Engineering at the University of Toronto. His primary research interests are in operations research, optimization, and applied machine learning, with applications in healthcare, medicine, sustainability, and sports. He holds editorial roles in several journals including Operations Research, Management Science, and M&SOM. Recent honours include the 2025 CORS Practice Prize, the 2024 President’s Teaching Award from the University of Toronto, first place in the 2024 MIT Sloan Sports Analytics Conference research paper competition, the 2023 INFORMS Prize for Teaching OR/MS Practice, the 2023 Pierskalla Best Paper Award from INFORMS, and first place in the 2022 INFORMS Case Competition. Professor Chan received his B.Sc. in Applied Mathematics from UBC and his Ph.D. in Operations Research from MIT. Before coming to Toronto, he was an Associate in the Chicago office of McKinsey and Company.