Compara mètodes

	Operador de Mitjana Simètrica de Maclaurin	Mitjana de Bonferroni (BM)	Tècnica per a l'Ordre de Preferència per Similitud a la Solució Ideal
Camp	Presa de decisions	Presa de decisions	Presa de decisions
Família	MCDM	MCDM	MCDM
Any d'origen	2014	1950	1981
Autor original	Variants developed from Maclaurin's mathematical theory	Bonferroni, C.	Hwang, C. L., Yoon, K.
Tipus	Symmetric mean aggregation operator for multiple criteria	Interrelationship-based aggregation — captures pairwise interactions	Distance-based (compromise)
Font seminal	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
Àlies	MSM, Maclaurin Mean	—	—
Relacionats	3	0	8
Resum	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.