Comparer des méthodes

	Plan d'expériences multi-réponses	Planification d'Expériences
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Process / pipeline
Année d'origine	1980 (desirability function formalization); DoE roots from Fisher, 1920s–1930s	1935
Auteur d'origine	Derringer & Suich (desirability function); Montgomery (systematic DoE integration)	Ronald A. Fisher
Type	Experimental optimization methodology	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 DoE, Multiple-response optimization, Multi-objective DoE, MRDoE	DOE, experimental design, factorial experimentation, planned experimentation
Apparentées	4	3
Résumé	Multi-response Design of Experiments (MRDoE) extends classical DoE to situations where several response variables must be optimized simultaneously. Rather than tuning factors for a single output, the experimenter fits separate regression or response-surface models for each response, then combines them — most often via Derringer and Suich's desirability function — into a single composite score that guides the search for factor settings satisfying all response targets 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.