A general framework for multiresponse optimization problems based on goal programming

Setting of process variables to meet a required specification of quality characteristic (or response variable) in a process, is one of the common problems in the process quality control. But generally there are more than one quality characteristics in the process and the experimenter attempts to optimize all of them simultaneously. Since response variables are different in some properties such as scale, measurement unit, type of optimality and their preferences, there are different approaches in model building and optimization of MRS problems. This study propose a general framework in MRS problems according to some existing works and some types of related decision makers and attempts to aggregate all of characteristics in one approach. The proposed framework contains four non-desirability parts of bias, response variation, errors in predictions and separation from responses' specific region. We demonstrate the proposed framework with two examples of the literature and the results has been discussed with comparing of mentioned existing works.