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An Approach to Consensus Measure Based on Possibility Degrees of PLTSs in Group Decision Making

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Abstract

The most desired alternative in group decision making (GDM) is the one that best reflects the consensus of the group. However, few studies have been conducted on consensus measure in GDM with probabilistic linguistic term sets (PLTSs). In this study, we propose an approach to consensus measure on the basis of the possibility degree of PLTSs in GDM. Firstly, we define the concept of degree of similarity between two PLTSs on the basis of possibility degrees. Then, we extend this concept to the consensus measure between the probabilistic linguistic information provided by group decision makers, and the relevant weight of each group decision maker is derived. Thereafter, we aggregate the PLTSs provided by each group decision maker and the alternatives are then ranked. Finally, two illustrative examples are proposed for the application of our method to evaluate the degree of consensus of collective preference values between experts and large group decision making problems with PLTSs. By comparison analysis, the advantages of the proposed method are verified.

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Acknowledgements

The authors are thankful to the editor and the anonymous reviewers for their valuable comments and constructive suggestions that greatly help the improvement of this paper. This work was supported by the National Natural Science Foundation of China (Nos. 71701037, 71701038 and 71601041).

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Authors and Affiliations

  1. School of Business Administration, Northeastern University, Shenyang, 110819, Liaoning, China

    Meng Zhao

  2. Northeastern University at Qinhuangdao, Qinhuangdao, 066004, China

    Meng Zhao, Qian Gao & Junyi Fang

  3. College of Science, Northeastern University, Shenyang, 110819, Liaoning, China

    Qirui Xiao

Authors
  1. Meng Zhao
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  2. Qian Gao
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  4. Qirui Xiao
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Correspondence to Meng Zhao.

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Zhao, M., Gao, Q., Fang, J. et al. An Approach to Consensus Measure Based on Possibility Degrees of PLTSs in Group Decision Making. Int. J. Fuzzy Syst. 20, 2257–2272 (2018). https://doi.org/10.1007/s40815-018-0464-9

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  • DOI: https://doi.org/10.1007/s40815-018-0464-9

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