Optimal experimental design for systems involving both quantitative and qualitative factors

Often in discrete-event simulation, factors being considered are qualitative such as machine type, production method, job release policy, and factory layout type. It is also often of interest to create a response surface (RS) metamodel for visualization of input-output relationships. Several methods have been proposed in the literature for RS metamodeling with qualitative factors but the resulting metamodels may be expected to predict poorly because of sensitivity to misspecification or bias. This paper proposes the use of the expected integrated mean squared error (EIMSE) criterion to construct alternative optimal experimental designs. This approach explicitly takes bias into account. We use a discrete-event simulation example from the literature, coded in ARENA™, to illustrate the proposed method and to compare metamodeling accuracy of alternative approaches computationally.