IEMS 453: Robust Optimization

Quarter Offered

Spring : TTH 2-3:20 PM ; Nohadani


Optimization under uncertainty typically requires a probability distribution of the uncertain parameters and/or variables.  While this approach has successfully solved many problems, its success is limited two-fold: many uncertainties do not follow a probabilistic distribution; and probabilistic method have not been developed with the goal of computational tractability.  In this context, robust optimization offers a new paradigm that allows to leap beyond these limitations and warrant solutions that are both optimal and immune against uncertainties.

The course objectives are: (i) reviewing stochastic programming; (ii) introducing robust optimization as an alternative approach to model uncertainties by replacing probabilistic assumptions by sets that are derived from the asymptotic implications of probability theory; (iii) exposing students to a number of applications; and (iv) providing an overview of computational tools.


Mathematical Optimization IEMS 450-1 or equivalent.  Knowledge of computational optimization modeling tools and solvers.  No required textbooks.  All material will be discussed in the class