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	Optymalizacja wspomagana pełnym projektem czynnikowym	Projektowanie Doświadczeń
Dziedzina	Planowanie eksperymentów	Planowanie eksperymentów
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	1980s–1990s (formalized with desirability functions by Derringer & Suich, 1980)	1935
Twórca	Integrated from D. C. Montgomery (DoE) and classical optimization literature	Ronald A. Fisher
Typ	Hybrid experimental-optimization workflow	Experimental planning framework
Źródło pierwotne	Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119492443	Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.
Inne nazwy	OA-FFD, full factorial with optimization, full factorial design with response optimization, DoE-optimization hybrid	DOE, experimental design, factorial experimentation, planned experimentation
Pokrewne	3	3
Podsumowanie	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.	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.