A fuzzy theoretic approach to simulation metamodeling

In this paper, we explore the potential application of fuzzy linear regression in developing simulation metamodels. It should be noted that the basic construct for simulation metamodels involves uncertainties and ambiguities that may be better addressed through fuzzy linear regression application. The solution techniques employed by fuzzy linear regression are very familiar, and the generation of fuzzy outputs may offer a wide range of solution space to the decision maker, thereby reducing the risk of making an incorrect economic decision. A numerical example is presented to show how a possibility distribution is used to capture the vagueness in a dependent variable for a regression metamodel.