Сравнение методов

	Фракционное факторное планирование с ассистированием моделирования	Методология поверхности отклика (RSM)
Область	Планирование эксперимента	Планирование эксперимента
Семейство	Process / pipeline	Hypothesis test
Год появления	FFD: 1950s; simulation integration: 1980s–2000s	1951
Автор метода	Box, Hunter & Hunter (FFD basis); Kleijnen and others (simulation integration)	George E. P. Box & K. B. Wilson
Тип	Experimental design with computational augmentation	Second-order polynomial response surface model
Основополагающий источник	Kleijnen, J. P. C. (2008). Design and Analysis of Simulation Experiments. Springer. ISBN: 978-0387718125	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.
Другие названия	SA-FFD, virtual fractional factorial design, computer-aided fractional factorial design, simulation-based FFD	RSM, Central Composite Design, Box-Behnken Design, CCD
Связанные	4	7
Сводка	Simulation-assisted fractional factorial design (SA-FFD) combines the statistical efficiency of fractional factorial experimentation with computerized simulation models to screen and estimate factor effects when physical experiments are too costly, hazardous, or time-consuming. A carefully chosen subset of factor-level combinations — the fractional factorial array — is executed inside a validated simulation model instead of (or alongside) a real process, dramatically reducing resource requirements while preserving the ability to identify main effects and low-order interactions.	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.