An Efficient Approach for Robustness based Design Optimization under Interval Uncertainty

This paper proposes a methodology for robustness- based design optimization under both aleatory (i.e., natural or physical variability) and epistemic uncertainty (i.e., imprecise probabilistic information). The proposed formulations specifically deal with epistemic uncertainty arising from multiple interval data. An efficient likelihood-based approach is used to represent the interval uncertainty, which is then used in the framework for robustness-based design optimization to achieve computational efficiency. The proposed robust design optimization methodology is illustrated using a general mathematical example problem.