Robust optimization: Difference between revisions

Revision as of 15:29, 10 December 2017

Revision as of 15:32, 10 December 2017

Line 112:

===Probabilistically robust optimization models===

These models quantify the uncertainty in the "true" value of the parameter of interest by probability distribution functions. They have been traditionally classified as [[stochastic programming]] and [[stochastic optimization]] models. Recently, probabilistically robust optimization has gained popularity by the introduction of rigorous theories such as [[scenario optimization]] able to quantify the robustness level of solutions obtained by randomized solutionsrandomization. These methods are also relevant to data-driven optimization methods.

===Robust counterpart===