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	Multi-Response Full Factorial Design	बहु-प्रतिक्रिया प्रतिक्रिया सतह पद्धति
क्षेत्र	प्रयोगात्मक अभिकल्प	प्रयोगात्मक अभिकल्प
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	1950s–1980s	1980 (Derringer & Suich desirability function); RSM roots ~1951 (Box & Wilson)
प्रवर्तक	Douglas C. Montgomery (factorial framework); Derringer & Suich (multi-response desirability optimization)	Derringer & Suich (desirability function approach); Myers & Montgomery (RSM framework)
प्रकार	Experimental design with multi-objective optimization	Experimental optimization technique
मौलिक स्रोत	Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119492443	Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.
उपनाम	MRFFD, multi-response FFD, multiple-response full factorial, multi-objective full factorial design	Multi-response RSM, MRSM, Multi-objective RSM, Multiple response optimization
संबंधित	3	6
सारांश	Multi-response full factorial design extends the classic full factorial experiment by measuring and jointly optimizing two or more response variables at the same time. Every combination of all factor levels is tested, providing complete main-effect and interaction information for each response. A desirability function or Pareto-front approach then reconciles competing responses into a single optimal factor setting, making this the method of choice when engineering or process goals involve trade-offs among several quality characteristics simultaneously.	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.