विधियों की तुलना करें

	बहु-प्रतिक्रिया भिन्नात्मक गुणनखंडीय डिज़ाइन	बहु-प्रतिक्रिया प्रतिक्रिया सतह पद्धति
क्षेत्र	प्रयोगात्मक अभिकल्प	प्रयोगात्मक अभिकल्प
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	1961 (fractional factorial foundation); 1980 (multi-response desirability approach)	1980 (Derringer & Suich desirability function); RSM roots ~1951 (Box & Wilson)
प्रवर्तक	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)
प्रकार	Experimental design with simultaneous multi-response optimization	Experimental optimization technique
मौलिक स्रोत	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.
उपनाम	MRFFD, multi-response FFD, multi-objective fractional factorial design, simultaneous multi-response fractional factorial	Multi-response RSM, MRSM, Multi-objective RSM, Multiple response optimization
संबंधित	4	6
सारांश	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.