পদ্ধতির তুলনা করুন

	বহু-প্রতিক্রিয়া পরীক্ষণ নকশা	রেসপন্স সারফেস মেথডোলজি (RSM)
ক্ষেত্র	পরীক্ষামূলক নকশা	পরীক্ষামূলক নকশা
পরিবার	Process / pipeline	Hypothesis test
উদ্ভবের বছর	1980 (desirability function formalization); DoE roots from Fisher, 1920s–1930s	1951
প্রবর্তক	Derringer & Suich (desirability function); Montgomery (systematic DoE integration)	George E. P. Box & K. B. Wilson
ধরন	Experimental optimization methodology	Second-order polynomial response surface model
মৌলিক উৎস	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.
অপর নাম	Multi-response DoE, Multiple-response optimization, Multi-objective DoE, MRDoE	RSM, Central Composite Design, Box-Behnken Design, CCD
সম্পর্কিত	4	7
সারসংক্ষেপ	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.