เปรียบเทียบวิธี

	Optimization-Assisted Fractional Factorial Design	ระเบียบวิธีพื้นผิวการตอบสนอง (Response Surface Methodology - RSM)
สาขาวิชา	การออกแบบการทดลอง	การออกแบบการทดลอง
ตระกูล	Process / pipeline	Hypothesis test
ปีกำเนิด	1960s–1980s (D-optimality: Kiefer & Wolfowitz 1959; coordinate-exchange: Meyer & Nachtsheim 1995)	1951
ผู้ริเริ่ม	A. C. Atkinson, A. N. Donev (optimality criteria); V. V. Federov (exchange algorithms)	George E. P. Box & K. B. Wilson
ประเภท	Optimal experimental design / computer-generated DOE	Second-order polynomial response surface model
แหล่งต้นตำรับ	Atkinson, A. C., Donev, A. N., & Tobias, R. D. (2007). Optimum Experimental Designs, with SAS. Oxford University Press. ISBN: 978-0199296606	Box, G. E. P. & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13(1), 1–45.
ชื่อเรียกอื่น	optimal fractional factorial design, algorithmically optimized FFD, computer-aided fractional factorial design, D-optimal fractional factorial design	RSM, Central Composite Design, Box-Behnken Design, CCD
ที่เกี่ยวข้อง	4	7
สรุป	Optimization-assisted fractional factorial design (OA-FFD) combines classical fractional factorial screening with algorithmic optimality criteria — such as D-, I-, or A-optimality — to construct experiment matrices that maximize statistical efficiency. Instead of relying solely on standard orthogonal-array tables, a computer algorithm selects the best subset of runs from a candidate set, enabling experimenters to handle irregular factor constraints, mixed factor types, and custom run sizes that standard tables cannot accommodate.	Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics.