Abstract
The conventional approach for optimizing multiresponse is fitting multiple response surface models and then analyzing them to obtain optimal settings for the input variables. However, it is difficult to obtain reliable response surface models when dealing with large amounts of data. In this article, a new approach to multiresponse optimization based on a classification and regression tree method is presented. Desirability functions are employed to simultaneously optimize the multiple responses. The case study of steel manufacturing company with large amounts of data shows that the proposed method obtains an optimal region in which multiple responses are simultaneously optimized.
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