A new optimization criterion for robust parameter design -: the case of target is best

Robust parameter design (RPD) is a set of techniques determining the levels of some set of controllable factors such that the sensitivity of the process to variations in another set of uncontrollable factors, the noise factors, is reduced; thus increasing the robustness of the process. The common assumption is that the noise factors can be controlled in an experimental environment. When this assumption does not hold true, a random effects model is applicable. Thus, if a fixed effects model is used for simplicity, an increase in the variance of the coefficient vector should be expected. In this article, we investigate the effects of this assumption, namely the effects of incorrect estimates of the regression parameters, on the final solution. Moreover, a new criterion is considered for optimization and the performance of this criterion is evaluated through a numerical example for the case of target is best.