Salīdzināt metodes

	Daudzreakciju eksperimentu plānošana	Metodoloģija virsmas atbildes (RSM)
Nozare	Eksperimentu plānošana	Eksperimentu plānošana
Saime	Process / pipeline	Hypothesis test
Izcelsmes gads	1980 (desirability function formalization); DoE roots from Fisher, 1920s–1930s	1951
Autors	Derringer & Suich (desirability function); Montgomery (systematic DoE integration)	George E. P. Box & K. B. Wilson
Tips	Experimental optimization methodology	Second-order polynomial response surface model
Pirmavots	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.
Citi nosaukumi	Multi-response DoE, Multiple-response optimization, Multi-objective DoE, MRDoE	RSM, Central Composite Design, Box-Behnken Design, CCD
Saistītās	4	7
Kopsavilkums	Multi-response Design of Experiments (MRDoE) extends classical DoE to situations where several response variables must be optimized simultaneously. Rather than tuning factors for a single output, the experimenter fits separate regression or response-surface models for each response, then combines them — most often via Derringer and Suich's desirability function — into a single composite score that guides the search for factor settings satisfying all response targets at once.	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.