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International Journal of Fuzzy Systems

, Volume 21, Issue 2, pp 407–420 | Cite as

Linguistic Intuitionistic Fuzzy Group Decision Making Based on Aggregation Operators

  • Ruiping Yuan
  • Jie Tang
  • Fanyong MengEmail author
Article

Abstract

This paper researches decision making with linguistic intuitionistic fuzzy variables. Several linguistic intuitionistic fuzzy operations are first defined. Then, several linguistic intuitionistic fuzzy aggregation operators are provided, including the linguistic intuitionistic fuzzy hybrid weighted arithmetical averaging operator, and the linguistic intuitionistic fuzzy hybrid weighted geometric mean operator. Considering the interactive characteristics between the weights of elements in a set, several linguistic intuitionistic fuzzy Shapley aggregation operators are presented, including the linguistic intuitionistic fuzzy hybrid Shapley arithmetical averaging operator, and the linguistic intuitionistic fuzzy hybrid Shapley geometric mean operator. To ensure the application reasonably, several desirable properties are discussed. When the weighting information is incompletely known, models for the optimal fuzzy and additive measures are constructed. After that, an approach to multi-criteria group decision making with linguistic intuitionistic fuzzy information is performed. Finally, a practical example about evaluating different types of engines is provided to illustrate the developed procedure.

Keywords

Group decision making Linguistic intuitionistic fuzzy variable Aggregation operator Shapley function 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 71571192 and 71671188), the Innovation-Driven Project of Central South University (No. 2018CX039), the Beijing Intelligent Logistics System Collaborative Innovation Center (No. 2018KF-06), the Fundamental Research Funds for the Central Universities of Central South University (No. 2018zzts094), and the State Key Program of National Natural Science of China (No. 71431006).

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Copyright information

© Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of InformationBeijing Wuzi UniversityBeijingChina
  2. 2.School of BusinessCentral South UniversityChangshaChina
  3. 3.School of Management and EconomicsNanjing University of Information Science and TechnologyNanjingChina

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