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	Datadriven multikriterieanalys	PROMETHEE II	Enkel Additiv Viktning
Ämnesområde	Beslutsfattande	Beslutsfattande	Beslutsfattande
Familj	MCDM	MCDM	MCDM
Ursprungsår	2015	1986	1967
Upphovsperson	Multiple authors	Brans, J. P., Vincke, Ph., Mareschal, B.	Fishburn, P. C.
Typ	Learning-based criteria weighting and aggregation	Preference function (net flow)	Additive utility (linear)
Ursprungskälla	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	Fishburn, P. C. (1967). Additive utilities with incomplete product sets: Application to priorities and assignments. Operations Research
Alias	Data-Driven MCDA	—	—
Närliggande	5	8	8
Sammanfattning	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.	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.