পদ্ধতির তুলনা করুন

	তথ্য-চালিত বহু-মানদণ্ড সিদ্ধান্ত বিশ্লেষণ	ELECTRE I	সরল যোজিত ওজন (Simple Additive Weighting)
ক্ষেত্র	সিদ্ধান্ত গ্রহণ	সিদ্ধান্ত গ্রহণ	সিদ্ধান্ত গ্রহণ
পরিবার	MCDM	MCDM	MCDM
উদ্ভবের বছর	2015	1968	1967
প্রবর্তক	Multiple authors	Roy, B.	Fishburn, P. C.
ধরন	Learning-based criteria weighting and aggregation	Concordance–discordance (crisp outranking)	Additive utility (linear)
মৌলিক উৎস	Греченко, Д. В. (2019). Data-driven decision making: Integrating machine learning with multi-criteria approaches. Computational Statistics & Data Analysis, 132, 127-143.	Roy, B. (1968). Classement et choix en présence de points de vue multiples (la méthode ELECTRE). Revue Française d'Informatique et de Recherche Opérationnelle	Fishburn, P. C. (1967). Additive utilities with incomplete product sets: Application to priorities and assignments. Operations Research
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.	ELECTRE (ELECTRE I — ELimination Et Choix Traduisant la REalité) is a outranking multi-criteria decision-making (MCDM) method introduced by Roy, B. in 1968. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.	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.