Bandingkan metode

	Desain Faktorial Fraksional Berbantuan Optimasi	Desain Eksperimen
Bidang	Desain Eksperimen	Desain Eksperimen
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	1960s–1980s (D-optimality: Kiefer & Wolfowitz 1959; coordinate-exchange: Meyer & Nachtsheim 1995)	1935
Pencetus	A. C. Atkinson, A. N. Donev (optimality criteria); V. V. Federov (exchange algorithms)	Ronald A. Fisher
Tipe	Optimal experimental design / computer-generated DOE	Experimental planning framework
Sumber perintis	Atkinson, A. C., Donev, A. N., & Tobias, R. D. (2007). Optimum Experimental Designs, with SAS. Oxford University Press. ISBN: 978-0199296606	Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.
Alias	optimal fractional factorial design, algorithmically optimized FFD, computer-aided fractional factorial design, D-optimal fractional factorial design	DOE, experimental design, factorial experimentation, planned experimentation
Terkait	4	3
Ringkasan	Optimization-assisted fractional factorial design (OA-FFD) combines classical fractional factorial screening with algorithmic optimality criteria — such as D-, I-, or A-optimality — to construct experiment matrices that maximize statistical efficiency. Instead of relying solely on standard orthogonal-array tables, a computer algorithm selects the best subset of runs from a candidate set, enabling experimenters to handle irregular factor constraints, mixed factor types, and custom run sizes that standard tables cannot accommodate.	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.