Methoden vergleichen

	Multi-response Fractional Factorial Design	Multi-response Response Surface Methodology
Fachgebiet	Versuchsplanung	Versuchsplanung
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1961 (fractional factorial foundation); 1980 (multi-response desirability approach)	1980 (Derringer & Suich desirability function); RSM roots ~1951 (Box & Wilson)
Urheber	George E.P. Box, J. Stuart Hunter, and William G. Hunter (fractional factorial basis); Derringer & Suich (multi-response desirability extension)	Derringer & Suich (desirability function approach); Myers & Montgomery (RSM framework)
Typ	Experimental design with simultaneous multi-response optimization	Experimental optimization technique
Wegweisende Quelle	Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.	Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.
Aliasnamen	MRFFD, multi-response FFD, multi-objective fractional factorial design, simultaneous multi-response fractional factorial	Multi-response RSM, MRSM, Multi-objective RSM, Multiple response optimization
Verwandt	4	6
Zusammenfassung	Multi-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.	Multi-response Response Surface Methodology (MRSM) extends classical RSM to situations where an experiment generates two or more response variables that must be optimized simultaneously. Rather than tuning factor settings for a single output, MRSM fits a separate second-order polynomial model for each response, then combines them — most commonly via Derringer and Suich's desirability function — to find factor settings that satisfy all objectives at once.