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Data-driven Preference Learning Methods for Sorting Problems with Multiple Temporal Criteria
This study presents novel preference learning approaches to multiple criteria sorting problems in the presence of temporal criteria.
Selection of a representative sorting model in a preference disaggregation setting: a review of existing procedures, new proposals, and experimental comparison
They exploit stochastic acceptabilities and maximize the support given to the resulting assignments by all feasible sorting models.
Recommending Multiple Criteria Decision Analysis Methods with A New Taxonomy-based Decision Support System
This decision support system helps analysts answering a recurring question in decision science: Which is the most suitable Multiple Criteria Decision Analysis method (or a subset of MCDA methods) that should be used for a given Decision-Making Problem (DMP)?.
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