Comparer des méthodes

	Plan d'expériences multi-réponses	Méthodologie des surfaces de réponse (RSM)
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Hypothesis test
Année d'origine	1980 (desirability function formalization); DoE roots from Fisher, 1920s–1930s	1951
Auteur d'origine	Derringer & Suich (desirability function); Montgomery (systematic DoE integration)	George E. P. Box & K. B. Wilson
Type	Experimental optimization methodology	Second-order polynomial response surface model
Source fondatrice	Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.	Box, G. E. P. & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13(1), 1–45.
Alias	Multi-response DoE, Multiple-response optimization, Multi-objective DoE, MRDoE	RSM, Central Composite Design, Box-Behnken Design, CCD
Apparentées	4	7
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.	Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics.