Engineering design often involves the optimization of different
competing objectives. A further significant step to realistic
multiobjective designs is to take into account uncertainties for
finding robust optimal solutions.
As the costs of a multi-objective optimization are already very high, it is important to use efficient approaches for optimization and uncertainty quantification. We apply a nonintrusive polynomial chaos approach for uncertainty quantification and the deterministic Epsilon -Constraint method for multi-objective aerodynamic shape optimization in SU2. We present the use of one-shot methods for optimization in this context and discuss the use of robustness measures.