Comparar métodos

	Diseño de Experimentos Asistido por Optimización	Metodología de Superficie de Respuesta (RSM)
Campo	Diseño experimental	Diseño experimental
Familia	Process / pipeline	Hypothesis test
Año de origen	1980 (desirability approach); broader integration through 1990s–2000s	1951
Autor original	Derringer & Suich (desirability function); extended by Myers, Montgomery, and Anderson-Cook	George E. P. Box & K. B. Wilson
Tipo	Hybrid experimental-optimization method	Second-order polynomial response surface model
Fuente seminal	Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.	Box, G. E. P. & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13(1), 1–45.
Alias	OA-DoE, DoE with optimization, optimization-integrated DoE, multi-objective experimental optimization	RSM, Central Composite Design, Box-Behnken Design, CCD
Relacionados	4	7
Resumen	Optimization-assisted design of experiments (OA-DoE) couples a structured experimental plan with a mathematical optimization engine to locate factor settings that simultaneously satisfy multiple response objectives. Rather than stopping at fitting a response surface model, the analyst applies desirability functions, genetic algorithms, or other optimizers to the fitted model to identify the global or near-global optimum across all responses of interest.	Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics.