Multi-response Optimization on Integrated Location and Dispersion Effects

With the advance of response surface optimization, an extended approach of integrated average and standard deviation of multiple responses is addressed in this paper. In addition to the location effect of the process performance, the dispersion effect should also be taken into account with the development of products. The multi-response optimization problem is discussed under a specific paradigm of non-linear programming (NLP). The combination of the mean square error and standard deviation is used to construct the objective of the mathematical model. Meanwhile, the scaling issue in multi-response optimization is resolved through the standardization of the numerical data. An NLP approach is proposed to seek the compromised optimum of the mathematical model. The illustrative examples are used to verify the proposed approach and compare to the existing methods in the literature.