So sánh phương pháp

	Thiết kế Phân Thừa Nhiều Đáp Ứng	Phương pháp Bề mặt Đáp ứng Đa phản hồi
Lĩnh vực	Thiết kế thí nghiệm	Thiết kế thí nghiệm
Họ	Process / pipeline	Process / pipeline
Năm ra đời	1961 (fractional factorial foundation); 1980 (multi-response desirability approach)	1980 (Derringer & Suich desirability function); RSM roots ~1951 (Box & Wilson)
Người khởi xướng	George E.P. Box, J. Stuart Hunter, and William G. Hunter (fractional factorial basis); Derringer & Suich (multi-response desirability extension)	Derringer & Suich (desirability function approach); Myers & Montgomery (RSM framework)
Loại	Experimental design with simultaneous multi-response optimization	Experimental optimization technique
Công trình gốc	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.
Tên gọi khác	MRFFD, multi-response FFD, multi-objective fractional factorial design, simultaneous multi-response fractional factorial	Multi-response RSM, MRSM, Multi-objective RSM, Multiple response optimization
Liên quan	4	6
Tóm tắt	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.	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.