Robust optimization: Difference between revisions

Revision as of 17:11, 9 October 2010

Revision as of 17:12, 9 October 2010

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== Classification ==

There are a number of classification criteria for robust optimization problems/models. In particular, one can distinguish between problems dealing with '''local''' and '''global''' models of robustness; and between '''probabilistic''' and '''non-probabilistic''' models of robustness. Modern robust optimization deals primarily with non-probabilistic models of robustness that are [[worst case]] oriented and as such usually deploy [[Wald's_maximin_model|Wald's maximin models]].

In one of the '''non-probabilistic''' models, Erfani and Utyuzhnikov<ref>Tohid Erfani and Sergei Utyuzhnikov. Handling Uncertainty and Finding Robust Pareto Frontier in Multiobjective Optimization Using Fuzzy Set Theory 51st AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference et al, Orlando, Florida, 2010</ref>, use the fuzzy variables in order to quantify the uncertainties within the design parameters. They introduce a Robust measure in contxt of [[multiobjective optimization]] to find the robust [[Pareto]] solutions.

== Local robustness ==