方法对比

	CRITIC-M	准则重要性通过准则间相关性 (CRITIC)	SWARA II
领域	决策	决策	决策
方法族	MCDM	MCDM	MCDM
起源年份	1995	1995	2010
提出者	Based on Diakoulaki et al.'s CRITIC; modified variants developed later	Diakoulaki, D., Mavrotas, G., Papayannakis, L.	Keršuliene, Zavadskas, and Turskis; extended by Zolfani et al.
类型	Objective weight derivation via correlation and variance	Statistical contrast intensity + correlation-based objective weighting	Expert-based stepwise weight derivation with ratio refinement
开创性文献	Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The CRITIC method. Computers & Operations Research, 22(7), 763-770.	Diakoulaki, D., Mavrotas, G., Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The CRITIC method. Computers & Operations Research	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.
别名	CRITIC-M, Modified CRITIC	—	SWARA II, SWARA 2
相关	3	8	4
摘要	CRITIC-M (Criteria Importance Through Intercriteria Correlation - Modified) is an objective weight derivation method that extends the classical CRITIC approach. It assigns weights to criteria based on two intrinsic properties of the decision matrix: variance (how much a criterion differentiates alternatives) and correlation (how much a criterion conflicts with or supplements others). Modified variants adjust the formulation to improve robustness or interpretability.	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.	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.