Some induced ordered weighted averaging operators and their use for solving group decision-making problems based on fuzzy preference relations

By Francisco Chiclana, Enrique Herrera-Viedma, Francisco Herrera, Sergio Alonso. European Journal of Operational Research, 2007

376 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/5906fb249d87eaba00850c5635e0eba2533bbfb2

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Citations in the corpus

A social network analysis trust-consensus based approach to group decision-making problems with interval-valued fuzzy reciprocal preference relations (2014, background): “Aggregation based on consensus level and trust degree In addition to the trust degree, the consistency degree or consensus level can also be regarded as reliable sources to derive experts’ aggregation weights [7, 24, 25, 42, 43].”
A web based consensus support system for group decision making problems and incomplete preferences (2010, methodology): “To do so, a modified version of the Additive Consistency IOWA (AC-IOWA) operator U [12,29,60] able to operate with fuzzy and linguistic preference relations is used:”
A statistical comparative study of different similarity measures of consensus in group decision making (2013, background): “Currently, at least 90 different families of aggregation operators have been studied [11, 12, 17, 19, 21, 35, 51, 52, 55, 56].”
Fuzzy Group Decision Making With Incomplete Information Guided by Social Influence (2018, neutral): “…importance degrees U , the collective FPR P that takes into account the importance of each expert can be obtained as In some GDM models, the importance degree of each expert is considered as known beforehand or provided by a reliable source while, in other cases, it is obtained in another way.”
A review of soft consensus models in a fuzzy environment (2014, methodology): “Weights are qualitative or quantitative values that can be assigned in several different ways [16]: a moderator can assign them directly, or the weights can be obtained automatically from the preference expressed by the experts (for example, the most consistent experts could received a higher…”
Trust based consensus model for social network in an incomplete linguistic information context (2015, methodology): “The application of the quantifier Q(r) = r 0.5 , used in literature to model the fuzzy linguistic quantifier 'most of' [11], yields the following weights w σ(1) = 0.73, w σ(2) = 0.27, and the final TS-IOWA orthopair of trust/distrust value from E 1 to E 4 of (t 14 , d 14 ) = (0.73 × 0.64 + 0.27 ×…”
A New Consensus Model for Group Decision Making Problems With Non-Homogeneous Experts (2014, background): “In the following subsections, both stages are described in detail.”
Managing the consensus in group decision making in an unbalanced fuzzy linguistic context with incomplete information (2010, methodology): “, pn} the values of the argument variable [12, 52, 53, 54].”