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This contribution presents an approach to account for imprecise data within an optimization task in view of engineering applications. In order to specify imprecise data the concept of imprecise probabilities is utilized, applying the generalized uncertainty model fuzzy randomness. Considering the fact, that the uncertainty affects both the objective function and the constraints, the optimum and the respective design is obtained imprecise. In view of decision making for engineering applications the optimization is converted to account for information reducing methods, e.g. determination of failure probabilities, defuzzification and robustness assessment. The introduced methods and algorithms are focused on a numerical treatment to solve nonlinear industry–sized problems. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) |
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