Salīdzināt metodes

	Uz datiem balstīta daudzkritēriju lēmumu analīze	ELECTRE I	PROMETHEE II	Vienkāršā aditīvā svēršana
Nozare	Lēmumu pieņemšana	Lēmumu pieņemšana	Lēmumu pieņemšana	Lēmumu pieņemšana
Saime	MCDM	MCDM	MCDM	MCDM
Izcelsmes gads	2015	1968	1986	1967
Autors	Multiple authors	Roy, B.	Brans, J. P., Vincke, Ph., Mareschal, B.	Fishburn, P. C.
Tips	Learning-based criteria weighting and aggregation	Concordance–discordance (crisp outranking)	Preference function (net flow)	Additive utility (linear)
Pirmavots	Греченко, Д. В. (2019). Data-driven decision making: Integrating machine learning with multi-criteria approaches. Computational Statistics & Data Analysis, 132, 127-143.	Roy, B. (1968). Classement et choix en présence de points de vue multiples (la méthode ELECTRE). Revue Française d'Informatique et de Recherche Opérationnelle	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
Citi nosaukumi	Data-Driven MCDA	—	—	—
Saistītās	5	8	8	8
Kopsavilkums	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.	ELECTRE (ELECTRE I — ELimination Et Choix Traduisant la REalité) is a outranking multi-criteria decision-making (MCDM) method introduced by Roy, B. in 1968. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.	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.