This paper develops a multiobjective optimization methodology for system design under uncertainty. Model-based reliability analysis methods are used to compute the probabilities of unsatisfactory performance at both component and system levels. Combined with several multiobjective optimization formulations, a versatile reliability-based design optimization (RBDO) approach is used to achieve a tradeoff between two objectives and to generate the Pareto frontier for decision making. The proposed RBDO approach uses direct reliability analysis to decouple the reliability and optimization iterations, instead of inverse first-order reliability analysis as other existing decoupled approaches. This helps to solve a wide variety of RBDO problems with competing objectives, especially when system-level reliability constraints need to be considered. The approach also allows more accurate methods to be used for reliability analysis, and reliability terms to be included in the objective function. Two important automotive quality objectives, related to the door closing effort (evaluated using component reliability analysis) and the wind noise (evaluated using system reliability analysis), are used to illustrate the proposed method.
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