So sánh phương pháp

	Phương pháp Bề mặt Đáp ứng Hỗ trợ Mô phỏng	Phương pháp Bề mặt Phản hồi Mạnh mẽ
Lĩnh vực	Thiết kế thí nghiệm	Thiết kế thí nghiệm
Họ	Process / pipeline	Process / pipeline
Năm ra đời	1951 (RSM); simulation integration widely adopted from 1980s onward	1990
Người khởi xướng	Box & Wilson (RSM foundation); Kleijnen and others for simulation-based extensions	G. G. Vining and Raymond H. Myers (dual response formulation)
Loại	Experimental optimization method	Experimental optimization technique
Công trình gốc	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	Vining, G. G., & Myers, R. H. (1990). Combining Taguchi and response surface philosophies: A dual response approach. Journal of Quality Technology, 22(1), 38–45.
Tên gọi khác	SA-RSM, simulation-based RSM, computer simulation RSM, metamodel-assisted RSM	Robust RSM, dual response surface methodology, robust parameter design via RSM, mean-variance RSM
Liên quan	6	3
Tóm tắt	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.	Robust Response Surface Methodology (Robust RSM) is an experimental optimization strategy that simultaneously fits two regression models — one for the mean response and one for its variance (or standard deviation) — across a designed experiment. By jointly optimizing these dual surfaces, engineers identify factor settings that hit a performance target while minimizing process variability, combining the empirical model-building power of classical RSM with the variance-reduction goals of robust parameter design.