Sammenlign metoder

	Datadrevet multi-kriterie analyse	Enkel additiv vekting	Teknikk for rangering etter likhet med idealløsning
Fagfelt	Beslutningstaking	Beslutningstaking	Beslutningstaking
Familie	MCDM	MCDM	MCDM
Opprinnelsesår	2015	1967	1981
Opphavsperson	Multiple authors	Fishburn, P. C.	Hwang, C. L., Yoon, K.
Type	Learning-based criteria weighting and aggregation	Additive utility (linear)	Distance-based (compromise)
Opprinnelig kilde	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
Alias	Data-Driven MCDA	—	—
Relaterte	5	8	8
Sammendrag	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.