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International Journal for Uncertainty Quantification
Facteur d'impact: 3.259 Facteur d'impact sur 5 ans: 2.547 SJR: 0.417 SNIP: 0.8 CiteScore™: 1.52

ISSN Imprimer: 2152-5080
ISSN En ligne: 2152-5099

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International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.2017019627
pages 135-154


Ying-Ying Li
School of Business, Central South University, Changsha, Hunan Province, 410083
Hongyu Zhang
School of Business, Central South University, Changsha 410083, China
Jian-Qiang Wang
School of Economics and Management, Hubei University of Automotive Technology, Shiyan 442002, China


Motivated by the reality that humans tend to convey their views using natural language, which is always indeterminate, imprecise, incomplete, and inconsistent, this paper introduces the concept of linguistic neutrosophic sets (LNSs), in which truth-membership, falsity-membership, and indeterminacy-membership are represented as linguistic terms. In order to compare any two linguistic neutrosophic numbers (LNNs), this paper defines the expected function, accuracy function, and certainty function. Subsequently, the operations for LNNs are provided based on linguistic scale functions. Then, two aggregation operators for fusing linguistic neutrosophic information are proposed, including the linguistic neutrosophic geometric Heronian mean (LNGHM) operator and the linguistic neutrosophic prioritized geometric Heronian mean (LNPGHM) operator. Moreover, this paper develops two new methods for addressing multicriteria decision-making (MCDM) problems in which the interrelationships among individual data are considered under linguistic neutrosophic environments. A practical example concerning low-carbon supplier selection is provided and the influence of different parameters is discussed. Finally, a comparison analysis is presented to verify the effectiveness and feasibility of the proposed methods.