Comparer des méthodes

	Méthodologie de surface de réponse assistée par optimisation	Méthodologie des surfaces de réponse (RSM)
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Hypothesis test
Année d'origine	1951 (RSM); 1980 (desirability-function optimization formalized)	1951
Auteur d'origine	Derringer & Suich (desirability function); Box & Wilson (RSM foundation)	George E. P. Box & K. B. Wilson
Type	Hybrid experimental-optimization framework	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	OA-RSM, RSM with optimization, desirability-based RSM, multi-response RSM optimization	RSM, Central Composite Design, Box-Behnken Design, CCD
Apparentées	5	7
Résumé	Optimization-assisted RSM couples a second-order response surface model with a mathematical optimization routine — most commonly Derringer and Suich's desirability function, but also genetic algorithms or gradient-based solvers — to locate the factor settings that simultaneously satisfy multiple quality or performance objectives. The result is a data-driven recommendation for optimal process or product conditions, supported by a polynomial model fitted to a structured experimental design.	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.