The origins of robustoptimization date back to the establishment of modern decision theory in the 1950s and the use of worst case analysis and Wald's maximin model as a tool for the treatment of severe uncertainty.
Robustoptimization is not restricted to linear programming. Many results are available for robust counterparts of other convex optimization problems with various types of uncertainty sets.
This paper considers RobustOptimization (RO), a more recent approach to optimization under uncertainty, in which the uncertainty model is not stochastic, but rather deterministic and set-based.
Robust optimization is a type of optimization that seeks to find a solution that is optimal under a set of possible scenarios or uncertainties. It differs from stochastic programming in that it does not require a probability distribution to be specified for the uncertain parameters.
Explore robustoptimization principles, frameworks, and algorithms to build resilient models that perform under data uncertainty and worst-case scenarios.
Robustoptimization is an emerging area in research that allows addressing different optimization problems and specifically industrial optimization problems where there is a degree of uncertainty in some of the variables involved.
Unlike traditional optimization methods that assume precise data, Robust Optimization acknowledges the inherent variability in data and aims to find solutions that remain effective under a range of possible scenarios.
This chapter presents the robustoptimization (RO) perspective. RO models require the constraints to be satisfied and the objective value insensitive (i.e., robust) to data ambiguity.
In robustoptimization, the modeler aims to find decisions that are optimal for the worst-case realization of the uncertainties within a given set [2]. Robustoptimization dates back to the beginning of modern decision theory in the 1950’s.
Robustoptimization is a young and active research field that has been mainly developed in the last 15 years. Robustoptimization is very useful for practice, since it is tailored to the information at hand, and it leads to computationally tractable formulations.
