Title
Interval-Valued Probabilistic Hesitant Fuzzy Set-Based Framework For Group Decision-Making With Unknown Weight Information
Abstract
This paper aims at presenting a new decision framework under an interval-valued probabilistic hesitant fuzzy set (IVPHFS) context with fully unknown weight information. At first, the weights of the attributes are determined by using the interval-valued probabilistic hesitant deviation method. Later, the DMs' weights are determined by using a recently proposed evidence theory-based Bayesian approximation method under the IVPHFS context. The preferences are aggregated by using a newly extended generalized Maclaurin symmetric mean operator under the IVPHFS context. Further, the alternatives are prioritized by using an interval-valued probabilistic hesitant complex proportional assessment method. From the proposed framework, the following significances are inferred; for example,it uses a generalized preference structure that provides ease and flexibility to the decision-makers(DMs)during preference elicitation; weights are calculated systematically to mitigate inaccuracies and subjective randomness; interrelationship among attributes are effectively captured; and alternatives are prioritized from different angles by properly considering the nature of the attributes. Finally, the applicability of the framework is validated by using green supplier selection for a leading bakery company, and from the comparison, it is observed that the framework isuseful, practical and systematicfor rational decision-makingand robust and consistentfrom sensitivity analysis of weights and Spearman correlation of rank values, respectively.
Year
DOI
Venue
2021
10.1007/s00521-020-05160-7
NEURAL COMPUTING & APPLICATIONS
Keywords
DocType
Volume
Bayesian approximation, COPRAS method, Deviation method, Evidence theory, Hesitant fuzzy set, Maclaurin symmetric mean
Journal
33
Issue
ISSN
Citations 
7
0941-0643
3
PageRank 
References 
Authors
0.37
22
4