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Multiple attribute group decision-making methods based on trapezoidal fuzzy two-dimension linguistic power generalized aggregation operators

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Abstract

On the basis of two-dimension uncertain linguistic variables, in this paper, we further presented a trapezoidal fuzzy two-dimension linguistic variable in which the first dimensional linguistic uncertain information is extended to trapezoidal fuzzy number. First, the definition, operational laws, characteristics, expectation, comparative method and distance of trapezoidal fuzzy two-dimension linguistic information are proposed. Then, the trapezoidal fuzzy two-dimension linguistic power generalized aggregation operator and the trapezoidal fuzzy two-dimension linguistic power generalized weighted aggregation (TF2DLPGWA) operator are developed, and some properties and special cases of these operators are analyzed. Furthermore, based on the TF2DLPGWA operator and the comparative formula of the trapezoidal fuzzy two-dimension linguistic variables, an approach to group decision making with trapezoidal fuzzy two-dimension linguistic variables is established. Finally, an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness.

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Acknowledgments

This paper is supported by the National Natural Science Foundation of China (No. 71471172 and No. 71271124), the Humanities and Social Sciences Research Project of Ministry of Education of China (No. 13YJC630104), Shandong Provincial Social Science Planning Project (No.13BGLJ10), Graduate education innovation projects in Shandong Province (SDYY12065) and The National Soft Science Project (2014GXQ4D192). The author also would like to express appreciation to the anonymous reviewers and Editors for their very helpful comments that improved the paper.

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Correspondence to Peide Liu.

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Communicated by V. Loia.

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Li, Y., Wang, Y. & Liu, P. Multiple attribute group decision-making methods based on trapezoidal fuzzy two-dimension linguistic power generalized aggregation operators. Soft Comput 20, 2689–2704 (2016). https://doi.org/10.1007/s00500-015-1668-7

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