Multiple response surface optimization with correlated data

Setting of process variables to meet the required specification of quality characteristics is an important problem in the process quality control. There are often several conflicts in quality characteristics, which should be simultaneously satisfied. These types of problems are called “Multiple Response Optimization” (MRO). When quality characteristics are correlated, MRO problems may become increasingly difficult. In design of experiments, identifying covariates effects could reduce error and uncovered variances as well as give more insight about the process. This study aims to identify process variables to consider correlated covariates and correlated quality characteristics. It also accommodates dispersion effects and specification limits as well as location effects in a unified framework based on desirability functions. The features of the proposed method are investigated and the results are compared with some existing techniques by applying two numerical examples.