Comparar métodos

	Metodología de Superficie de Respuesta Asistida por Simulación	Metodología de Superficie de Respuesta Asistida por Optimización
Campo	Diseño experimental	Diseño experimental
Familia	Process / pipeline	Process / pipeline
Año de origen	1951 (RSM); simulation integration widely adopted from 1980s onward	1951 (RSM); 1980 (desirability-function optimization formalized)
Autor original	Box & Wilson (RSM foundation); Kleijnen and others for simulation-based extensions	Derringer & Suich (desirability function); Box & Wilson (RSM foundation)
Tipo	Experimental optimization method	Hybrid experimental-optimization framework
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	Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.
Alias	SA-RSM, simulation-based RSM, computer simulation RSM, metamodel-assisted RSM	OA-RSM, RSM with optimization, desirability-based RSM, multi-response RSM optimization
Relacionados	6	5
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.	Optimization-assisted RSM couples a second-order response surface model with a mathematical optimization routine — most commonly Derringer and Suich's desirability function, but also genetic algorithms or gradient-based solvers — to locate the factor settings that simultaneously satisfy multiple quality or performance objectives. The result is a data-driven recommendation for optimal process or product conditions, supported by a polynomial model fitted to a structured experimental design.