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حوزهتصمیم‌گیریتصمیم‌گیری
خانوادهMCDMMCDM
سال پیدایش20151967
پدیدآورMultiple authorsFishburn, P. C.
نوعLearning-based criteria weighting and aggregationAdditive utility (linear)
منبع بنیادینГреченко, Д. В. (2019). Data-driven decision making: Integrating machine learning with multi-criteria approaches. Computational Statistics & Data Analysis, 132, 127-143. link ↗Fishburn, P. C. (1967). Additive utilities with incomplete product sets: Application to priorities and assignments. Operations Research DOI ↗
نام‌های دیگرData-Driven MCDA
مرتبط58
خلاصهData-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.
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ScholarGateمقایسهٔ روش‌ها: Data-Driven MCDA · SAW. بازیابی‌شده در 2026-06-15 از https://scholargate.app/fa/compare