Robust optimization has proven to be an effective paradigm in tackling optimization problems with uncertainty. Its efficacy spans a variety of settings with uncertainties bounded in predetermined sets. In many applications however, the uncertainty may be affected by decisions or by other uncertainties. We take a step towards generalizing robust optimization and capturing these models by leveraging variable uncertainty sets which allow the set parameters to be functions of decision variables or of other uncertainties in the problem. We illustrate the properties of this framework through a unit commitment application.
This seminar will be streamed. https://anlpress.cels.anl.gov/cels-seminars/