Group decision making with a fuzzy linguistic majority
Fuzzy Sets and Systems
A sequential selection process in group decision making with a linguistic assessment approach
Information Sciences—Intelligent Systems: An International Journal
Direct approach processes in group decision making using linguistic OWA operators
Fuzzy Sets and Systems
A model of consensus in group decision making under linguistic assessments
Fuzzy Sets and Systems
A rational consensus model in group decision making using linguistic assessments
Fuzzy Sets and Systems
Linguistic decision analysis: steps for solving decision problems under linguistic information
Fuzzy Sets and Systems - Special issue on soft decision analysis
Generalized chi square method for the estimation of weights
Journal of Optimization Theory and Applications
On Compatibility of Interval Fuzzy Preference Relations
Fuzzy Optimization and Decision Making
Information Sciences—Informatics and Computer Science: An International Journal
Induced uncertain linguistic OWA operators applied to group decision making
Information Fusion
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Expert Systems with Applications: An International Journal
Multi-criteria decision making with incomplete linguistic preference relations
ACOS'07 Proceedings of the 6th Conference on WSEAS International Conference on Applied Computer Science - Volume 6
AIKED'08 Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases
AIKED'08 Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases
Incomplete linguistic preference relations to evaluate multimedia authoring system
WSEAS Transactions on Mathematics
Computing with words in decision making: foundations, trends and prospects
Fuzzy Optimization and Decision Making
An integrated model-based interactive approach to FMAGDM with incomplete preference information
Fuzzy Optimization and Decision Making
A web based consensus support system for group decision making problems and incomplete preferences
Information Sciences: an International Journal
ACS'10 Proceedings of the 10th WSEAS international conference on Applied computer science
Solving multi-criteria decision making with incomplete linguistic preference relations
Expert Systems with Applications: An International Journal
On group decision making with four formats of incomplete preference relations
Computers and Industrial Engineering
A new incomplete preference relations based approach to quality function deployment
Information Sciences: an International Journal
The ordinal consistency of a fuzzy preference relation
Information Sciences: an International Journal
Ordering based decision making - A survey
Information Fusion
Hi-index | 0.00 |
Various linguistic preference relations, including incomplete linguistic preference relation, consistent incomplete linguistic preference relation and acceptable incomplete linguistic preference relation, are introduced. Some desirable properties of the incomplete linguistic preference relation are studied. Based on the operational laws of the linguistic evaluation scale, and the acceptable incomplete linguistic preference relation with the least judgments, we develop a simple and practical method for constructing a consistent complete linguistic preference relation by using the additive transitivity property. The method not only relieves the decision maker of time pressure and makes sufficiently using of the provided preference information, but also maintains the decision maker's consistency level and avoids checking the consistency of linguistic preference relation. Furthermore, an approach to multi-person decision making based on incomplete linguistic preference relations is developed. The approach fuses all the consistent complete linguistic preference relations, constructed by using the individual acceptable incomplete linguistic preference relations with the least judgments, into a collective complete linguistic preference relation, and then the overall information corresponding to each decision alternative is aggregated. Finally, an illustrative example is given.