방법 비교

	다중 반응 반응 표면 방법론	최적화 지원 반응 표면 방법론
분야	실험설계	실험설계
계열	Process / pipeline	Process / pipeline
기원 연도	1980 (Derringer & Suich desirability function); RSM roots ~1951 (Box & Wilson)	1951 (RSM); 1980 (desirability-function optimization formalized)
창시자	Derringer & Suich (desirability function approach); Myers & Montgomery (RSM framework)	Derringer & Suich (desirability function); Box & Wilson (RSM foundation)
유형	Experimental optimization technique	Hybrid experimental-optimization framework
원전	Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.	Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.
별칭	Multi-response RSM, MRSM, Multi-objective RSM, Multiple response optimization	OA-RSM, RSM with optimization, desirability-based RSM, multi-response RSM optimization
관련	6	5
요약	Multi-response Response Surface Methodology (MRSM) extends classical RSM to situations where an experiment generates two or more response variables that must be optimized simultaneously. Rather than tuning factor settings for a single output, MRSM fits a separate second-order polynomial model for each response, then combines them — most commonly via Derringer and Suich's desirability function — to find factor settings that satisfy all objectives at once.	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.