Comparar métodos

	Diseño de Experimentos Asistido por Optimización	Diseño de Experimentos
Campo	Diseño experimental	Diseño experimental
Familia	Process / pipeline	Process / pipeline
Año de origen	1980 (desirability approach); broader integration through 1990s–2000s	1935
Autor original	Derringer & Suich (desirability function); extended by Myers, Montgomery, and Anderson-Cook	Ronald A. Fisher
Tipo	Hybrid experimental-optimization method	Experimental planning framework
Fuente seminal	Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.	Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.
Alias	OA-DoE, DoE with optimization, optimization-integrated DoE, multi-objective experimental optimization	DOE, experimental design, factorial experimentation, planned experimentation
Relacionados	4	3
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.	Design of Experiments (DOE) is a systematic framework for planning, conducting, and analyzing controlled experiments to determine how multiple input factors simultaneously affect one or more responses. Introduced by Ronald A. Fisher in 1935, DOE allows researchers and engineers to identify causal relationships, quantify factor effects, and find optimal settings efficiently — using far fewer runs than one-factor-at-a-time approaches. It is foundational in engineering, manufacturing, agriculture, and applied sciences.