Methoden vergleichen

	Simulation-Assundierte Response Surface Methodology	Optimierungsgestützte Response Surface Methodology
Fachgebiet	Versuchsplanung	Versuchsplanung
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1951 (RSM); simulation integration widely adopted from 1980s onward	1951 (RSM); 1980 (desirability-function optimization formalized)
Urheber	Box & Wilson (RSM foundation); Kleijnen and others for simulation-based extensions	Derringer & Suich (desirability function); Box & Wilson (RSM foundation)
Typ	Experimental optimization method	Hybrid experimental-optimization framework
Wegweisende Quelle	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	Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.
Aliasnamen	SA-RSM, simulation-based RSM, computer simulation RSM, metamodel-assisted RSM	OA-RSM, RSM with optimization, desirability-based RSM, multi-response RSM optimization
Verwandt	6	5
Zusammenfassung	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.	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.