Optimization of Multiple Response Variables Using the Desirability Function and a Bayesian Predictive Distribution

This paper proposes the modification of a technique of simultaneous optimization of multiple response variables that works using a Bayesian predictive distribution, to incorporate different weights to the response variables according to their importance in the products. To achieve this, the desirability function has been incorporated to the original proposal. This research shows by representing different scenarios in one case study taken from literature, that the highest desirabilities and in turn the proposed optimum values in the process operating conditions always moved toward regions where the response variables with the highest weights had the best results, at the expense of performance in variables with the lowest weights.

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