Comparar métodos

	Metodología de Superficie de Respuesta Asistida por Simulación	Diseño Box-Behnken
Campo	Diseño experimental	Diseño experimental
Familia	Process / pipeline	Process / pipeline
Año de origen	1951 (RSM); simulation integration widely adopted from 1980s onward	1960
Autor original	Box & Wilson (RSM foundation); Kleijnen and others for simulation-based extensions	George E. P. Box and Donald W. Behnken
Tipo	Experimental optimization method	Response surface design (incomplete three-level factorial)
Fuente seminal	Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley. ISBN: 978-1118916025	Box, G. E. P., & Behnken, D. W. (1960). Some new three level designs for the study of quantitative variables. Technometrics, 2(4), 455–475.
Alias	SA-RSM, simulation-based RSM, computer simulation RSM, metamodel-assisted RSM	BBD, Box-Behnken, Box-Behnken RSM design, three-level incomplete factorial design
Relacionados	6	3
Resumen	Simulation-assisted response surface methodology (SA-RSM) combines computer simulation models — such as finite element analysis, computational fluid dynamics, or discrete-event simulation — with the statistical framework of response surface methodology to efficiently map, model, and optimize system responses. Instead of running physical experiments, the researcher executes simulation runs at design points prescribed by an RSM design, fits a polynomial metamodel (surrogate) to the simulation outputs, and uses that metamodel to locate optimal factor settings.	The Box-Behnken design (BBD) is an efficient response surface methodology design that fits a full second-order polynomial model using three levels of each factor. Introduced by Box and Behnken in 1960, it places experimental points at the midpoints of the edges of a hypercube and at the center, avoiding the corner points where all factors are simultaneously at their extreme levels. This structure makes BBD particularly attractive when extreme-level combinations are physically impossible, costly, or unsafe to test.