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	डेटा-संचालित बहु-मानदंड निर्णय विश्लेषण	PROMETHEE II	आदर्श समाधान के साथ समानता द्वारा वरीयता के लिए तकनीक
क्षेत्र	निर्णयन	निर्णयन	निर्णयन
परिवार	MCDM	MCDM	MCDM
उद्भव वर्ष	2015	1986	1981
प्रवर्तक	Multiple authors	Brans, J. P., Vincke, Ph., Mareschal, B.	Hwang, C. L., Yoon, K.
प्रकार	Learning-based criteria weighting and aggregation	Preference function (net flow)	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.	Brans, J. P., Vincke, Ph., Mareschal, B. (1986). How to select and how to rank projects: The PROMETHEE method. European Journal of Operational 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.	PROMETHEE (PROMETHEE II — Preference Ranking Organisation METHod for Enrichment of Evaluations) is a outranking multi-criteria decision-making (MCDM) method introduced by Brans, J. P., Vincke, Ph., Mareschal, B. in 1986. 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.