Sammenlign metoder

	CRiteria Importance Through Intercriteria Correlation	MEREC-G	SWARA II
Fagfelt	Beslutningstaking	Beslutningstaking	Beslutningstaking
Familie	MCDM	MCDM	MCDM
Opprinnelsesår	1995	2021	2010
Opphavsperson	Diakoulaki, D., Mavrotas, G., Papayannakis, L.	Keshavarz Ghorabaee, Hosseinzadeh Lotfi et al.	Keršuliene, Zavadskas, and Turskis; extended by Zolfani et al.
Type	Statistical contrast intensity + correlation-based objective weighting	Objective weight derivation via removal impact assessment	Expert-based stepwise weight derivation with ratio refinement
Opprinnelig kilde	Diakoulaki, D., Mavrotas, G., Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The CRITIC method. Computers & Operations Research	Keshavarz Ghorabaee, M., Hosseinzadeh Lotfi, F., Behzadi, M., & Sałabun, W. (2021). MEREC: A new multi-criteria model to evaluate wind farm locations. Sustainability, 12(15), 6136.	Keršuliene, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of rational dispute resolution method by evaluating opposing parties' interest in civil litigation. Journal of Civil Engineering and Management, 16(3), 412-422.
Alias	—	MEREC-G, Generalized MEREC	SWARA II, SWARA 2
Relaterte	8	3	4
Sammendrag	CRITIC (CRiteria Importance Through Intercriteria Correlation) is a weight objective multi-criteria decision-making (MCDM) method introduced by Diakoulaki, D., Mavrotas, G., Papayannakis, L. in 1995. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.	MEREC-G (Method Based on Removal Effects of Criteria - Generalized) is an objective weight derivation method that assigns weights based on the impact of removing each criterion from the decision analysis. The core idea is that important criteria, when removed, cause large changes in the final ranking. Generalized variants extend the original MEREC to various aggregation logic and decision contexts.	SWARA II (Step-wise Weight Assessment Ratio Analysis - Improved) is an enhanced variant of the SWARA method for deriving criterion weights from expert assessments. Instead of requiring pairwise comparisons or absolute weight assignments, SWARA II asks experts to rank criteria, then assess the relative importance of each criterion compared to the next-ranked one. Improved variants enhance robustness and interpretability of weight derivation.