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.