Salīdzināt metodes

	Optimizācijā balstīta pilna faktoriālā eksperimenta plānošana	Metodoloģija virsmas atbildes (RSM)
Nozare	Eksperimentu plānošana	Eksperimentu plānošana
Saime	Process / pipeline	Hypothesis test
Izcelsmes gads	1980s–1990s (formalized with desirability functions by Derringer & Suich, 1980)	1951
Autors	Integrated from D. C. Montgomery (DoE) and classical optimization literature	George E. P. Box & K. B. Wilson
Tips	Hybrid experimental-optimization workflow	Second-order polynomial response surface model
Pirmavots	Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119492443	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.
Citi nosaukumi	OA-FFD, full factorial with optimization, full factorial design with response optimization, DoE-optimization hybrid	RSM, Central Composite Design, Box-Behnken Design, CCD
Saistītās	3	7
Kopsavilkums	Optimization-assisted full factorial design is a structured engineering workflow that runs a complete full factorial experiment — covering every combination of factor levels — and then applies a formal optimization method to identify the factor settings that best satisfy one or more performance targets. It combines the exhaustive data coverage of full factorial design with numerical or analytical optimization to turn experimental results into actionable optimal configurations.	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.