A model for selecting an ERP system based on linguistic information processing
Information Systems
Decision making under uncertainty with fuzzy targets
Fuzzy Optimization and Decision Making
IEEE Transactions on Fuzzy Systems
Computing with words in decision making: foundations, trends and prospects
Fuzzy Optimization and Decision Making
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Information Sciences: an International Journal
Fuzzy decision-making based on likelihood-based comparison relations
IEEE Transactions on Fuzzy Systems - Special section on computing with words
Expert Systems with Applications: An International Journal
A probabilistic model for linguistic multi-expert decision making involving semantic overlapping
Expert Systems with Applications: An International Journal
Treating fuzziness in subjective evaluation data
Information Sciences: an International Journal
Information Sciences: an International Journal
Adaptive consensus support model for group decision making systems
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
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This paper proposes a multiexpert decision-making (MEDM) method with linguistic assessments, making use of the notion of random preferences and a so-called satisfactory principle. It is well known that decision-making problems that manage preferences from different experts follow a common resolution scheme composed of two phases: an aggregation phase that combines the individual preferences to obtain a collective preference value for each alternative; and an exploitation phase that orders the collective preferences according to a given criterion, to select the best alternative/s. For our method, instead of using an aggregation operator to obtain a collective preference value, a random preference is defined for each alternative in the aggregation phase. Then, based on a satisfactory principle defined in this paper, that says that it is perfectly satisfactory to select an alternative as the best if its performance is as at least "good" as all the others under the same evaluation scheme, we propose a linguistic choice function to establish a rank ordering among the alternatives. Moreover, we also discuss how this linguistic decision rule can be applied to the MEDM problem in multigranular linguistic contexts. Two application examples taken from the literature are used to illuminate the proposed techniques.