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• W2058538852 abstract "Aims: The aim of this paper is to develop the 2-tuple linguistic Bonferroni mean and the weighted 2-tuple linguistic Bonferroni mean. Study Design: Some desirable properties and special cases of the developed operators are discussed. The geometric Bonferroni mean (GBM) is a generalization of the Bonferroni mean and geometric mean. In this paper, we also investigate the GBM under 2-tuple linguistic environments. We develop the 2-tuple linguistic geometric Bonf erroni mean and the weighted 2tuple linguistic geometric Bonferroni mean. We investiga te some fundamental properties and special cases of them. Place and Duration of Study: The Bonferroni Mean (BM) operator is a traditional mean t ype aggregation operator, which can capture the expressed interrelat ionship of the individual arguments and which is only suitable to aggregate crisp data. Methodology: This paper extends the BM operator to 2-tuple linguistic en vironments. Results: Based on these operators, we develop two approaches for m ultiple attribute group decision making with 2-tuple linguistic information. Conclusion: Two numerical examples are provided to illustrate the effec tiveness and practicality of the proposed approaches." @default.
• W2058538852 created "2016-06-24" @default.
• W2058538852 creator A5007264715 @default.
• W2058538852 creator A5075673161 @default.
• W2058538852 date "2014-01-10" @default.
• W2058538852 modified "2023-10-18" @default.
• W2058538852 title "2-tuple Linguistic Bonferroni Mean Operators and Their Application to Multiple Attribute Group Decision Making" @default.
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• W2058538852 doi "https://doi.org/10.9734/bjmcs/2014/9590" @default.
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