方法对比

	数据驱动的多标准决策分析	简单加权求和法	理想解相似优劣排序法
领域	决策	决策	决策
方法族	MCDM	MCDM	MCDM
起源年份	2015	1967	1981
提出者	Multiple authors	Fishburn, P. C.	Hwang, C. L., Yoon, K.
类型	Learning-based criteria weighting and aggregation	Additive utility (linear)	Distance-based (compromise)
开创性文献	Греченко, Д. В. (2019). Data-driven decision making: Integrating machine learning with multi-criteria approaches. Computational Statistics & Data Analysis, 132, 127-143.	Fishburn, P. C. (1967). Additive utilities with incomplete product sets: Application to priorities and assignments. Operations Research	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
别名	Data-Driven MCDA	—	—
相关	5	8	8
摘要	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.	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.	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.