Vertaile menetelmiä

	Data-Driven Multi-Criteria Decision Analysis	PROMETHEE II	Yksinkertainen additiivinen painotus
Tieteenala	Päätöksenteko	Päätöksenteko	Päätöksenteko
Menetelmäperhe	MCDM	MCDM	MCDM
Syntyvuosi	2015	1986	1967
Kehittäjä	Multiple authors	Brans, J. P., Vincke, Ph., Mareschal, B.	Fishburn, P. C.
Tyyppi	Learning-based criteria weighting and aggregation	Preference function (net flow)	Additive utility (linear)
Alkuperäislähde	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
Rinnakkaisnimet	Data-Driven MCDA	—	—
Liittyvät	5	8	8
Tiivistelmä	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.