Comparar métodos

	Diseño Compuesto Central Asistido por Optimización	Metodología de Superficie de Respuesta (RSM)
Campo	Diseño experimental	Diseño experimental
Familia	Process / pipeline	Hypothesis test
Año de origen	1951 (CCD); optimization coupling formalized 1970s–1990s	1951
Autor original	Box & Wilson (CCD, 1951); optimization integration by Myers, Montgomery & colleagues	George E. P. Box & K. B. Wilson
Tipo	Experimental design with mathematical optimization	Second-order polynomial response surface model
Fuente seminal	Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2009). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (3rd ed.). Wiley. ISBN: 978-0470174463	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	CCD with optimization, optimized CCD, RSM-CCD optimization, central composite design with response optimization	RSM, Central Composite Design, Box-Behnken Design, CCD
Relacionados	3	7
Resumen	Optimization-assisted central composite design (CCD) combines the rotatable, second-order experimental layout of central composite design with mathematical optimization algorithms — typically desirability functions, response surface optimization, or metaheuristics — to find the factor settings that simultaneously maximize, minimize, or hit target values for one or more response variables. It is the most widely applied response-surface optimization workflow in chemical, pharmaceutical, food science, and manufacturing engineering.	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.