Porównaj metody

	Optymalizacja wspomagana projektowaniem eksperymentów	Central Composite Design
Dziedzina	Planowanie eksperymentów	Planowanie eksperymentów
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	1980 (desirability approach); broader integration through 1990s–2000s	1951
Twórca	Derringer & Suich (desirability function); extended by Myers, Montgomery, and Anderson-Cook	George E. P. Box and K. B. Wilson
Typ	Hybrid experimental-optimization method	Response surface experimental design
Źródło pierwotne	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.
Inne nazwy	OA-DoE, DoE with optimization, optimization-integrated DoE, multi-objective experimental optimization	CCD, Box-Wilson design, central composite response surface design, rotatable central composite design
Pokrewne	4	3
Podsumowanie	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.	Central Composite Design (CCD) is a second-order response surface design that allows researchers to efficiently fit a full quadratic model relating multiple continuous input factors to one or more response variables. Introduced by Box and Wilson in 1951, it combines a factorial (or fractional factorial) core, axial (star) points, and center-point replicates into a single unified design, making it the most widely used design for process optimization in engineering, chemistry, and manufacturing.