Abstract
A metamodel is a surrogate model of physical processes or simulation models, and is used to represent the input-output relationships of complicated systems. Metamodeling represents the process of constructing a metamodel, and Response Surface Methodology (RSM) is one of the most well-known techniques used to produce a metamodel. RSM has some inherent advantages over other metamodeling techniques due to statistical experimental design fundamentals, that made this technique more effective and reliable. The fidelity of a metamodel provides useful insights to understand parameters of interest in a system and assists in system optimization, and this technique is known as metamodel-based optimization (MBO). MBO is applicable to both physical experiments and simulation experiments. Based on a conceptual MBO model, we introduce three RSM-based frameworks that allow for efficient development of a metamodel based on variables of interest that supports MBO in certain cases. In our research, three empirical studies have been used to validate the viability of proposed frameworks among physical experiments and simulation experiments, respectively. Finally, we provide the conclusion and describe future research topics.