Comparer des méthodes

	Méthodologie de surface de réponse assistée par optimisation	Planification d'Expériences
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Process / pipeline
Année d'origine	1951 (RSM); 1980 (desirability-function optimization formalized)	1935
Auteur d'origine	Derringer & Suich (desirability function); Box & Wilson (RSM foundation)	Ronald A. Fisher
Type	Hybrid experimental-optimization framework	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	OA-RSM, RSM with optimization, desirability-based RSM, multi-response RSM optimization	DOE, experimental design, factorial experimentation, planned experimentation
Apparentées	5	3
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.	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.