Compară metode

	Operatorul Mediei Simetrice Maclaurin	Bonferroni Mean (BM)	Tehnică de Ordine de Preferință prin Similaritate cu Soluția Ideală
Domeniu	Luarea deciziilor	Luarea deciziilor	Luarea deciziilor
Familie	MCDM	MCDM	MCDM
Anul apariției	2014	1950	1981
Autorul original	Variants developed from Maclaurin's mathematical theory	Bonferroni, C.	Hwang, C. L., Yoon, K.
Tip	Symmetric mean aggregation operator for multiple criteria	Interrelationship-based aggregation — captures pairwise interactions	Distance-based (compromise)
Sursa seminală	Qin, J., Liu, X., & Pedrycz, W. (2014). An extended TOPSIS model for multiple attribute decision making with interval-valued intuitionistic fuzzy information. International Journal of Fuzzy Systems, 16(1), 99-113.	Bonferroni, C. (1950). Sulle medie multiple di potenze. Bollettino dell'Unione Matematica Italiana	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
Denumiri alternative	MSM, Maclaurin Mean	—	—
Înrudite	3	0	8
Rezumat	The Maclaurin Symmetric Mean (MSM) operator is an aggregation method that combines multiple criteria or attribute values using symmetric mean functions. Unlike simple averaging, MSM captures interactions between criteria and enables flexible sensitivity to criterion magnitudes through a parameter λ. It is particularly useful in fuzzy multi-criteria decision analysis and handles both individual and joint effects of criteria.	BONFERRONI-MEAN (Bonferroni Mean (BM)) is a aggregation multi-criteria decision-making (MCDM) method introduced by Bonferroni, C. in 1950. 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.