Search

The consensus reaching process is a dynamic and iterative process for improving group's consensus level before making a final decision in group decision-making (GDM). As the experts will express their opinions under their own intellectual level from different aspects, it is natural that the experts’ weights should reflect their judgment information. This paper proposes a dynamic way to adjust weights of decision-makers (DMs) automatically when they are asked to give original judgment information for GDM problems, in which the DMs express their judgment information by hesitant fuzzy preference relations (HFPRs). Two indices, an individual consensus index of hesitant fuzzy preference relation (ICIHFPR) and a group consensus index of hesitant fuzzy preference relation (GCIHFPR), are introduced. Normalisation of HFPRs with different numbers of possible values is taken into consideration for better computation and comparison. An iterative consensus reaching algorithm is presented with DMs’ weighting vector changing in each consensus reaching process and the process terminates until both theICIHFPRandGCIHFPRare controlled within predefined thresholds. Finally, an example is illustrated and comparative analyses demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Considering that there may exist some interactions between membership function and non-membership function of different dual hesitant fuzzy sets (DHFSs), in this paper we present some new operational laws from the probability point of view and give a geometric interpretation of the new operations. Based on which, a new class of dual hesitant fuzzy aggregation operators are developed, including dual hesitant fuzzy weighted interaction averaging operator, dual hesitant fuzzy weighted interaction geometric operator, dual hesitant fuzzy ordered weighted interaction averaging operator, dual hesitant fuzzy ordered weighted interaction geometric operator, dual hesitant fuzzy hybrid interaction averaging operator, and dual hesitant fuzzy hybrid interaction geometric operator. The properties of these aggregation operators are investigated. The key advantage of these operators is that the interactions between non-membership function and membership function of different DHFSs are considered, and much more information provided by decision makers (DMs) has been taken into account. Additionally, approaches to multiple attributes decision-making are given based on the aggregation operators under hesitant fuzzy environment and an example is given to illustrate the application of DHFSs in group forecasting. [ABSTRACT FROM PUBLISHER]