A generalized multiple response surface methodology for complex computer simulation applications

This work provides a generalization of the traditional response surface methodology (RSM) that can be applied to complex, multiobjective simulation studies. These problems involve a larger number of input variables, multiple measures of performance, and complex systems relationships. This multiple RSM approach capitalizes on the underlying learning philosophy of the traditional RSM while benefiting from other knowledge discovery concepts and data mining techniques. Furthermore it does not require the restrictive assumptions of the traditional RSM nor does it restrict the analyst to the traditional RSM techniques. Based on a variation of (Brown and Schamburg 2004) and (Schamburg 2004), a brief description of the generalized approach is provided. Then, the multiple response techniques are shown through an example application.