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Wielowyjściowy ułamkowy plan czynnikowy×Projektowanie Doświadczeń×
DziedzinaPlanowanie eksperymentówPlanowanie eksperymentów
RodzinaProcess / pipelineProcess / pipeline
Rok powstania1961 (fractional factorial foundation); 1980 (multi-response desirability approach)1935
TwórcaGeorge E.P. Box, J. Stuart Hunter, and William G. Hunter (fractional factorial basis); Derringer & Suich (multi-response desirability extension)Ronald A. Fisher
TypExperimental design with simultaneous multi-response optimizationExperimental planning framework
Źródło pierwotneDerringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219. DOI ↗Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd. link ↗
Inne nazwyMRFFD, multi-response FFD, multi-objective fractional factorial design, simultaneous multi-response fractional factorialDOE, experimental design, factorial experimentation, planned experimentation
Pokrewne43
PodsumowanieMulti-response fractional factorial design (MRFFD) applies a resolution-efficient fractional factorial experiment to study multiple response variables simultaneously. By running only a carefully chosen fraction of the full factorial treatment combinations, the experimenter gathers enough information to fit individual response models for each output and then optimize all responses jointly — typically via a composite desirability function — while keeping the number of experimental runs tractable.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.
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ScholarGatePorównaj metody: Multi-response Fractional Factorial Design · Design of experiments. Pobrano 2026-06-20 z https://scholargate.app/pl/compare