Linganisha mbinu

	Ubunifu wa Nusu-Factorial Usaidiziwa na uhisimulizi	Muundo wa Majaribio
Nyanja	Muundo wa Majaribio	Muundo wa Majaribio
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	FFD: 1950s; simulation integration: 1980s–2000s	1935
Mwanzilishi	Box, Hunter & Hunter (FFD basis); Kleijnen and others (simulation integration)	Ronald A. Fisher
Aina	Experimental design with computational augmentation	Experimental planning framework
Chanzo asilia	Kleijnen, J. P. C. (2008). Design and Analysis of Simulation Experiments. Springer. ISBN: 978-0387718125	Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.
Majina mbadala	SA-FFD, virtual fractional factorial design, computer-aided fractional factorial design, simulation-based FFD	DOE, experimental design, factorial experimentation, planned experimentation
Zinazohusiana	4	3
Muhtasari	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.	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.