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Generalized Hybrid Aggregation Operators Based on the 2-Dimension Uncertain Linguistic Information for Multiple Attribute Group Decision Making

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Abstract

With respect to the multiple attribute decision making problems in which the attribute values take the form of the 2-dimension uncertain linguistic information, a new method based on the generalized hybrid operators is proposed. Firstly, the definition, properties, expectations and ranking method of 2-dimension uncertain linguistic information are introduced, and the operational laws the 2-dimension uncertain linguistic information are defined. Then some aggregation operators, including 2-dimension uncertain linguistic generalized weighted average operator, 2-dimension uncertain linguistic generalized ordered weighted average operator, and 2-dimension uncertain linguistic generalized hybrid weighted average operator, are developed, and some properties and special cases of them are also discussed. Finally, these operators are applied to multi-criteria decision making and an illustrative example is given to verify the developed approach and to demonstrate its effectiveness.

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Acknowledgments

This paper is supported by the National Natural Science Foundation of China (Nos. 71271124, 71471172), the national soft science research project (2014GXQ4D192), the Natural Science Foundation of Shandong Province (No. ZR2011FM036), Shandong Provincial Social Science Planning Project (No.13BGLJ10), and Graduate education innovation projects in Shandong Province (SDYY12065).

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Authors and Affiliations

  1. School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan, 250014, Shandong, China

    Peide Liu, Li He & Xiaocun Yu

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  1. Peide Liu
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  2. Li He
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  3. Xiaocun Yu
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Correspondence to Peide Liu.

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Liu, P., He, L. & Yu, X. Generalized Hybrid Aggregation Operators Based on the 2-Dimension Uncertain Linguistic Information for Multiple Attribute Group Decision Making. Group Decis Negot 25, 103–126 (2016). https://doi.org/10.1007/s10726-015-9434-x

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