Mathematical optimization is still dominated by deterministic models and corresponding algorithms. But many engineering and industrial optimization challenges demand for more realistic modelling including stochastic effects. Common Monte-Carlo methods are too expensive for engineering applications. Polynomial chaos expansions have found to be an efficient mathematical approach for several industrial applications, like turbomachinery design and production failure reduction.