Comparer des méthodes

	Méthodologie de surface de réponse assistée par simulation	Planification d'Expériences
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Process / pipeline
Année d'origine	1951 (RSM); simulation integration widely adopted from 1980s onward	1935
Auteur d'origine	Box & Wilson (RSM foundation); Kleijnen and others for simulation-based extensions	Ronald A. Fisher
Type	Experimental optimization method	Experimental planning framework
Source fondatrice	Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley. ISBN: 978-1118916025	Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.
Alias	SA-RSM, simulation-based RSM, computer simulation RSM, metamodel-assisted RSM	DOE, experimental design, factorial experimentation, planned experimentation
Apparentées	6	3
Résumé	Simulation-assisted response surface methodology (SA-RSM) combines computer simulation models — such as finite element analysis, computational fluid dynamics, or discrete-event simulation — with the statistical framework of response surface methodology to efficiently map, model, and optimize system responses. Instead of running physical experiments, the researcher executes simulation runs at design points prescribed by an RSM design, fits a polynomial metamodel (surrogate) to the simulation outputs, and uses that metamodel to locate optimal factor settings.	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.