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The Course Will Review Modeling and Solution Methods For Optimization Under Uncertainty. The Course Includes The Following Topics# Robust And Distributionally Robust Optimization, Stochastic Optimization, Chance Constraints, Data-driven Optimization, Solution Methods Including Robust-counterparts and Iterative Methods. Learning Outcomes# At The End of The Course The Student Will Be Able To# 1. Understand The Challenges of Modeling and Solving Optimization Problems With Uncertain Parameters, Including The Limitations of The Various Modeling Techniques and The Computational Challenges Associated With The Various Solution Methods. 2. Formulate Robust Optimization Models For Single- and Multi-stage Optimization Problems With Uncertainty. 3. Solve Different Robust Optimization Models By Using Robust Counterparts and Iterative Methods. 4. Understand How to Incorporate Data in Modeling Uncertainty and The Statistical Meaning of Such Models. 5. Implement The Methods Learned On Real-world Optimization Problems With Uncertainty, Through The Characterization of The Uncertainty And Identification of The Appropriate Modeling and Solution Techniques.

Faculty: Data and Decision Sciences
|Undergraduate Studies |Graduate Studies

Pre-required courses

96327 - Nonlinear Models in Operations Research or 98311 - Optimization 1