Desirability function approach: A review and performance evaluation in adverse conditions

Adverse conditions in terms of quality of predictions and robustness are simulated to evaluate the ability of desirability-based methods for yielding compromise solutions with desired response's properties. The method's solutions are assessed at optimal variable settings with respect to bias, quality of predictions and robustness through optimization measures, and the usefulness of those measures to select the compromise solution is evaluated. Three examples with different features in terms of responses variance are used and the performance of various analysis methods is compared. Results show that a less sophisticated desirability-based method can compete with other methods designed to perform well under adverse conditions and that the optimization measures justify its use in real life problems.