방법 비교

	데이터 기반 다기준 의사결정 분석	단순 가중치 합 (Simple Additive Weighting)	이상해결책과의 유사성에 따른 선호도 순위 결정 기법
분야	의사결정	의사결정	의사결정
계열	MCDM	MCDM	MCDM
기원 연도	2015	1967	1981
창시자	Multiple authors	Fishburn, P. C.	Hwang, C. L., Yoon, K.
유형	Learning-based criteria weighting and aggregation	Additive utility (linear)	Distance-based (compromise)
원전	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
별칭	Data-Driven MCDA	—	—
관련	5	8	8
요약	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.