This article reviews the design and analysis of simulation experiments. It focusses on analysis via either low-order polynomial regression or Kriging (also known as Gaussian process) metamodels. The type of metamodel determines the design of the experiment, which determines the input combinations of the simulation experiment. For example, a …first-order polynomial metamodel requires a "resolution-III" design, whereas Kriging may use Latin hypercube sampling. Polynomials of fi…rst or second order require resolution III, IV, V, or "central composite" designs. Before applying either regression or Kriging, sequential bifurcation may be applied to screen a great many inputs. Optimization of the simulated system may use either a sequence of low-order polynomials known as response surface methodology (RSM) or Kriging models …tted through sequential designs including e¢ cient global optimization (EGO). The review includes robust optimization, which accounts for uncertain simulation inputs.
|Place of Publication||Tilburg|
|Publisher||CentER, Center for Economic Research|
|Number of pages||34|
|Publication status||Published - 6 Jul 2015|
|Name||CentER Discussion Paper|
- robustness and sensitivity
Kleijnen, J. P. C. (2015). Regression and Kriging Metamodels with Their Experimental Designs in Simulation: Review. (CentER Discussion Paper; Vol. 2015-035). Tilburg: CentER, Center for Economic Research.