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	Datadrevet multi-kriterie beslutningsanalyse	Technique for Order of Preference by Similarity to Ideal Solution
Fagområde	Beslutningstagning	Beslutningstagning
Familie	MCDM	MCDM
Oprindelsesår	2015	1981
Ophavsperson	Multiple authors	Hwang, C. L., Yoon, K.
Type	Learning-based criteria weighting and aggregation	Distance-based (compromise)
Oprindelig kilde	Греченко, Д. В. (2019). Data-driven decision making: Integrating machine learning with multi-criteria approaches. Computational Statistics & Data Analysis, 132, 127-143.	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
Aliasser	Data-Driven MCDA	—
Relaterede	5	8
Resumé	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.	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.