Comprehensive minimum cost models for large scale group decision making with consistent fuzzy preference relations

2021 ◽  
Vol 215 ◽  
pp. 106780
Author(s):  
Rosa M. Rodríguez ◽  
Álvaro Labella ◽  
Bapi Dutta ◽  
Luis Martínez
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Large Scale ◽  
Group Decision ◽  
Minimum Cost ◽  
Cost Models ◽  
Preference Relations ◽  
Fuzzy Preference Relations ◽  
Fuzzy Preference

Consensus-based group decision-making methods with probabilistic dual hesitant fuzzy preference relations and their applications

2021 ◽  
pp. 1-18
Author(s):  
Juan Song ◽  
Zhiwei Ni ◽  
Feifei Jin ◽  
Wenying Wu ◽  
Ping Li
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Large Scale ◽  
Distance Measure ◽  
Group Decision ◽  
Uncertain Information ◽  
Location Selection ◽  
Preference Relations ◽  
Fuzzy Preference Relations ◽  
Fuzzy Preference

Probabilistic dual hesitant fuzzy sets (PDHFSs) have good flexibility and integrity in expressing fuzzy and uncertain information. However, some crucial problems related to PDHFSs remain unsolved, such as how to define probabilistic dual hesitant fuzzy preference relations (PDHFPRs) and solve group decision-making (GDM) problems with PDHFPRs. This paper establishes the concept of PDHFPRs and investigates consensus-based GDM methods with PDHFPRs. First, a new distance measure is proposed to quantify the difference between two PDHFPRs, which does not increase the virtual elements of membership and non-membership degrees, and can contain all distance combination of membership and non-membership elements. Therefore, the distance calculation results are not affected by the subjectivity of decision-makers (DMs). Second, the consensus measures for PDHFPRs are proposed, which are effective tool to measure the consensus level among DMs. Moreover, two consensus-based GDM methods are proposed, which can improve the group consensus level for PDHFPRs by changing the PDHFPR with the worst consensus level or modifying the weights of DMs. Finally, the proposed methods are applied to the location selection of large-scale industrial solid waste treatment facilities. The comparison with existing methods illustrates the validity and feasibility of the proposed methods.

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