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A group decision making with probability linguistic preference relations based on nonlinear optimization model and fuzzy cooperative games

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Abstract

The aim of the paper is to solve the group decision making problems which contain inconsistent probabilistic linguistic preference relations (PLPRs) and unknown expert weights. When the PLPRs are inconsistent, there are contradictories in the preference relations expressed by the experts. The evaluation value with contradictory information will bring out an incorrect consequence in decision making. Hence, this paper develops a novel consistency measure method to gauge the consistency level of PLPRs. Moreover, a nonlinear optimization model is newly constructed to optimize the inconsistent PLPRs. The proposed methods overcome the limitations in the existing methods and ameliorate the interpretation and complexities of inconsistency PLPRs revise strategies. Additionally, a weighting method using fuzzy cooperative games with PLPRs is put forward to derive the weight vector of experts. It helps to balance the deviations between the individual PLPRs and the group PLRP. At last, a numerical example illustration for physician selection is put forward to demonstrate the effectiveness of the proposed model and its practical applicability. The comparative analysis gives deep insights into the rationality of the proposed model.

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Acknowledgements

This work is supported partially by the National Natural Science Foundation of China (Nos. 71871229, 71771219 and 91846301).

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Authors and Affiliations

  1. School of Business, Central South University, Changsha, 410083, China

    Pei Liang, Junhua Hu, Bo Li, Yongmei Liu & Xiaohong Chen

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  1. Pei Liang
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  2. Junhua Hu
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  3. Bo Li
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  4. Yongmei Liu
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  5. Xiaohong Chen
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Correspondence to Junhua Hu.

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Liang, P., Hu, J., Li, B. et al. A group decision making with probability linguistic preference relations based on nonlinear optimization model and fuzzy cooperative games. Fuzzy Optim Decis Making 19, 499–528 (2020). https://doi.org/10.1007/s10700-020-09329-6

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