Comparer des méthodes

	Méthodologie des surfaces de réponse à réponses multiples	Planification d'Expériences
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Process / pipeline
Année d'origine	1980 (Derringer & Suich desirability function); RSM roots ~1951 (Box & Wilson)	1935
Auteur d'origine	Derringer & Suich (desirability function approach); Myers & Montgomery (RSM framework)	Ronald A. Fisher
Type	Experimental optimization technique	Experimental planning framework
Source fondatrice	Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.	Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.
Alias	Multi-response RSM, MRSM, Multi-objective RSM, Multiple response optimization	DOE, experimental design, factorial experimentation, planned experimentation
Apparentées	6	3
Résumé	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.	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.