Linguistic dynamic multicriteria decision making using symbolic linguistic computing models

In fuzzy environments, decision information is more suitable to be expressed in linguistic labels than exact numerical values. A linguistic dynamic multicriteria decision making (LDMCDM) problem consists of a finite set of periods in which a set of experts express their evaluations about a finite set of alternatives on a linguistic term set, to select the best alternative of the problem. To deal with linguistic information in LDMCDM, two main symbolic linguistic computing models have been applied: the 2-tuple and the virtual linguistic computing models. In this contribution, we review these symbolic computing models and also propose a resolution scheme for solving LDMCDM problems. Special emphasis is put into time-dependent aggregation operators due to they are crucial in this type of problems. Thereafter we apply them to a green supplier selection problem to stress the suitability of the proposed resolution scheme, and to analyse the results obtained with both models mainly in terms of representation of linguistic outcomes as well as their interpretability and accuracy. Eventually, some challenges are introduced for further research.

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