Assessing effectiveness of the various performance metrics for multi-response optimization using multiple regression
Several methods for optimization of multiple response problems using planned experimental data have been proposed in the literature. Among them, an integrated approach of multiple regression-based optimization using an overall performance criteria has become quite popular. In this article, we examine the effectiveness of five performance metrics that are used for optimization of multiple response problems. The usefulness of these performance metrics are compared with respect to a utility measure, namely, the expected total non-conformance (NC), for three experimental datasets taken from the literature. It is observed that multiple regression-based weighted signal-to-noise ratio as a performance metric is the most effective in finding an optimal solution for multiple response problems. (C) 2010 Elsevier Ltd. All rights reserved.
multiple regression multiple responses weighted signal-to-noise ratio taguchi method desirability function multivariate loss function