Choice processes for non-homogeneous group decision making in linguistic setting
Fuzzy Sets and Systems
Extensions of the TOPSIS for group decision-making under fuzzy environment
Fuzzy Sets and Systems
Aggregation of partial ordinal rankings: an interval goal programming approach
Computers and Operations Research
A context-dependent method for ordering fuzzy numbers using probabilites
Information Sciences—Informatics and Computer Science: An International Journal
An optimization method for integrating two kinds of preference information in group decision-making
Computers and Industrial Engineering - Special issue: Selected papers from the 27th international conference on computers & industrial engineering
Information Sciences—Informatics and Computer Science: An International Journal
A preference aggregation method through the estimation of utility intervals
Computers and Operations Research
Fuzzy multi-criteria evaluation of industrial robotic systems
Computers and Industrial Engineering
Fuzzy preference relations: Aggregation and weight determination
Computers and Industrial Engineering
A method for group decision making with multi-granularity linguistic assessment information
Information Sciences: an International Journal
Computers and Industrial Engineering
Computers and Industrial Engineering
Modeling the concept of majority opinion in group decision making
Information Sciences: an International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Computers and Industrial Engineering
Computers and Industrial Engineering
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The aim of this paper is to develop an approach to solving group decision-making problems, where the preference information on alternatives provided by experts is in the form of uncertain preference ordinals. In this paper, firstly, we give several definitions on uncertain preference ordinal. Then, to process uncertain preference ordinals, a decision matrix in the form of probabilities is constructed. Based on the decision matrix, a collective probability matrix on alternatives with regard to ranking positions is constructed. Furthermore, an optimization model is built based on the collective probability matrix, and the ranking of alternatives can be obtained by solving the model. Finally, two examples are used to illustrate the use of the proposed approach.