পদ্ধতির তুলনা করুন

	তথ্য-চালিত বহু-মানদণ্ড সিদ্ধান্ত বিশ্লেষণ	সরল যোজিত ওজন (Simple Additive Weighting)	Ideal Solution-এর সঙ্গে সাদৃশ্যের ভিত্তিতে পছন্দের ক্রম নির্ণয়ের কৌশল
ক্ষেত্র	সিদ্ধান্ত গ্রহণ	সিদ্ধান্ত গ্রহণ	সিদ্ধান্ত গ্রহণ
পরিবার	MCDM	MCDM	MCDM
উদ্ভবের বছর	2015	1967	1981
প্রবর্তক	Multiple authors	Fishburn, P. C.	Hwang, C. L., Yoon, K.
ধরন	Learning-based criteria weighting and aggregation	Additive utility (linear)	Distance-based (compromise)
মৌলিক উৎস	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
অপর নাম	Data-Driven MCDA	—	—
সম্পর্কিত	5	8	8
সারসংক্ষেপ	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.