Empirical model-building and response surface
Empirical model-building and response surface
Statistical tools for simulation practitioners
Statistical tools for simulation practitioners
Response surfaces: designs and analyses
Response surfaces: designs and analyses
Metamodeling: a state of the art review
WSC '94 Proceedings of the 26th conference on Winter simulation
Research issues in metamodeling
WSC '91 Proceedings of the 23rd conference on Winter simulation
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Simulation Modeling and Analysis
Simulation Modeling and Analysis
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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.