Võrdle meetodeid

	Andmetel põhinev mitmekriteeriumiline otsustusanalüüs	Lihtne liitkaalutlus	Tehnika eelistuste järjestamiseks ideaallahendusele sarnasuse järgi
Valdkond	Otsustamine	Otsustamine	Otsustamine
Perekond	MCDM	MCDM	MCDM
Tekkeaasta	2015	1967	1981
Looja	Multiple authors	Fishburn, P. C.	Hwang, C. L., Yoon, K.
Tüüp	Learning-based criteria weighting and aggregation	Additive utility (linear)	Distance-based (compromise)
Algallikas	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
Rööpnimetused	Data-Driven MCDA	—	—
Seotud	5	8	8
Kokkuvõte	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.