Models are prone to errors, often due to uncertain parameters. For optimization under uncertainty, the larger the amount of uncertain parameters, the higher the computational effort and the possibility of obtaining unrealistic results. In this contribution it is assumed that not all uncertain parameters need to be regarded and focus should be laid on a subset. As a first step in the algorithm, a parameter estimation is carried out to determine expected values, followed by a linear-dependency

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... lysis and a ranking of the uncertain parameters. Parameters with a high linear-dependency are fixed, while others are left uncertain. This is followed by a subset selection regarding the sensitivity of the parameters toward the model and toward a user-defined objective function. Thus, only parameters with the largest sensitivities are selected as uncertain parameters and considered for optimization under uncertainty. A case study is presented in which the algorithm is applied.