Robust optimization is a subset of optimization theory that deals with a certain measure of robustness vs uncertainty. This balance of robustness and uncertainty is represented as variability in the parameters of the problem at hand and or its solution [1]. In robust optimization, the modeler aims to find decisions that are optimal for the worst-case realization of the uncertainties within a given set [2]. Robust optimization dates back to the beginning of modern decision theory in the 1950’s. It became a discipline of its own in the 1970’s with paralleled development in other technological fields.

Last modified

• 11/30/2018

Creator

• Andre Ramirez-Cedeno

DOI

• https://doi.org/10.21985/N2176H

Keyword

• Robust
• Classical Optimization

Rights statement

• In Copyright