เปรียบเทียบวิธี

	การออกแบบแฟกทอเรียลแบบเศษส่วนหลายการตอบสนอง	ระเบียบวิธีพื้นผิวการตอบสนอง (Response Surface Methodology - RSM)
สาขาวิชา	การออกแบบการทดลอง	การออกแบบการทดลอง
ตระกูล	Process / pipeline	Hypothesis test
ปีกำเนิด	1961 (fractional factorial foundation); 1980 (multi-response desirability approach)	1951
ผู้ริเริ่ม	George E.P. Box, J. Stuart Hunter, and William G. Hunter (fractional factorial basis); Derringer & Suich (multi-response desirability extension)	George E. P. Box & K. B. Wilson
ประเภท	Experimental design with simultaneous multi-response optimization	Second-order polynomial response surface model
แหล่งต้นตำรับ	Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.	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	MRFFD, multi-response FFD, multi-objective fractional factorial design, simultaneous multi-response fractional factorial	RSM, Central Composite Design, Box-Behnken Design, CCD
ที่เกี่ยวข้อง	4	7
สรุป	Multi-response fractional factorial design (MRFFD) applies a resolution-efficient fractional factorial experiment to study multiple response variables simultaneously. By running only a carefully chosen fraction of the full factorial treatment combinations, the experimenter gathers enough information to fit individual response models for each output and then optimize all responses jointly — typically via a composite desirability function — while keeping the number of experimental runs tractable.	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.