A robust desirability function method for multi-response surface optimization considering model uncertainty

Abstract

A robust desirability function approach to simultaneously optimizing multiple responses is proposed. The approach considers the uncertainty associated with the fitted response surface model. The uniqueness of the proposed method is that it takes account of all values in the confidence interval rather than a single predicted value for each response and then defines the robustness measure for the traditional desirability function using the worst case strategy. A hybrid genetic algorithm is developed to find the robust optima. The presented method is compared with its conventional counterpart through an illustrated example from the literature.