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Academic Lecture: The Dao of Robustness

Speaker:  Prof. Melvyn Sim,  National University of Singapore

Moderator: Prof. Gang Kou, School of Business Administration

Time: 14:00-15:30 , Apr. 8, 2021

Platform: Tencent Meeting 856 207 699 (Password: 123456)

Organizers: School of Business Administration, Research Office

Speaker's Profile

Dr. Melvyn Sim is Professor and Provost's Chair at the Department of Analytics & Operations, NUS Business school. His research interests fall broadly under the categories of decision making and optimization under uncertainty with applications ranging from finance, supply chain management, healthcare to engineered systems. He is one of the active proponents of robust optimization and has given invited talks in this field at international conferences. Dr. Sim serves as a Department Editor of Manufacturing & Service Operations Management, and as an Associate Editor for Operations Research, Management Science, Mathematical Programming Computations and INFORMS Journal on Optimization.

Lecture Preview

We present a general framework for data-driven optimization called robustness optimization that favors solutions for which a risk-aware objective function would best attain an acceptable target even when the actual probability distribution deviates from the empirical distribution. Unlike robust optimization approaches, the decision maker does not have to size the ambiguity set, but specifies an acceptable target, or loss of optimality compared to the empirical optimization model, as a trade off for the model’s ability to withstand greater uncertainty. We axiomatize the decision criterion associated with robustness optimization, termed as the fragility measure, and present its representation theorem. Focusing on Wasserstein distance measure with l1-norm, we present tractable robustness optimization models for risk-based linear optimization, combinatorial optimization, and linear optimization problems with recourse. Serendipitously, the insights to the approximation also provide a recipe for approximating solutions for hard stochastic optimization problems without relatively complete recourse. We perform numerical studies on a portfolio optimization problem and a network lot-sizing problem. We show that the solutions to the robustness optimization models are more effective in improving the out-of-sample performance evaluated on a variety of metrics, hence alleviating the Optimizer’s Curse.