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	डेटा-संचालित बहु-मानदंड निर्णय विश्लेषण	सरल योगात्मक भारण	आदर्श समाधान के साथ समानता द्वारा वरीयता के लिए तकनीक
क्षेत्र	निर्णयन	निर्णयन	निर्णयन
परिवार	MCDM	MCDM	MCDM
उद्भव वर्ष	2015	1967	1981
प्रवर्तक	Multiple authors	Fishburn, P. C.	Hwang, C. L., Yoon, K.
प्रकार	Learning-based criteria weighting and aggregation	Additive utility (linear)	Distance-based (compromise)
मौलिक स्रोत	Греченко, Д. В. (2019). Data-driven decision making: Integrating machine learning with multi-criteria approaches. Computational Statistics & Data Analysis, 132, 127-143.	Fishburn, P. C. (1967). Additive utilities with incomplete product sets: Application to priorities and assignments. Operations Research	Hwang, C. L., Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications — A State-of-the-Art Survey. Lecture Notes in Economics and Mathematical Systems, Vol. 186, Springer-Verlag
उपनाम	Data-Driven MCDA	—	—
संबंधित	5	8	8
सारांश	Data-Driven MCDA is a hybrid framework that integrates machine learning and statistical learning into traditional multi-criteria decision analysis. Instead of eliciting weights from expert judgment, it learns criteria importance from historical decision data, enabling more scalable and empirically grounded decision support.	SAW (Simple Additive Weighting) is a ranking multi-criteria decision-making (MCDM) method introduced by Fishburn, P. C. in 1967. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.	TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is a ranking multi-criteria decision-making (MCDM) method introduced by Hwang, C. L., Yoon, K. in 1981. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.