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Group decision making with interval fuzzy preference relations based on DEA and stochastic simulation

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Abstract

This paper proposes an integrated approach to group decision making with interval fuzzy preference relations using data envelopment analysis (DEA) and stochastic simulation. A novel output-oriented CCR DEA model is proposed to obtain the priority vector for the consistency fuzzy preference relation, in which each of the alternatives is viewed as a decision-making unit. Meanwhile, we design a consistency adjustment algorithm for the inconsistent fuzzy preference relation. Furthermore, we build an optimization model to get the weights of each fuzzy preference relation based on maximizing group consensus. Then, an input-oriented DEA model is introduced to obtain the final priority vector of the alternatives. Finally, a stochastic group preference analysis method is developed by analyzing the judgments space, which is carried out by Monte Carlo simulation. A numerical example demonstrates that the proposed method is effective.

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Acknowledgements

The work was supported by National Natural Science Foundation of China (Nos. 71501002, 61502003, 71371011, 71771001, 71701001) and Anhui Provincial Natural Science Foundation (Nos. 1508085QG149, 1608085QF133).

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Authors and Affiliations

  1. School of Business, Anhui University, Hefei, 230601, Anhui, China

    Jinpei Liu

  2. MOE Key Laboratory of Intelligence Computing and Signal Processing, Anhui University, Hefei, 230601, Anhui, China

    Qin Xu

  3. School of Mathematical Sciences, Anhui University, Hefei, 230601, Anhui, China

    Huayou Chen, Ligang Zhou, Jiaming Zhu & Zhifu Tao

  4. Institute of Manufacturing Development, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Ligang Zhou

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  1. Jinpei Liu
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  2. Qin Xu
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  3. Huayou Chen
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  5. Jiaming Zhu
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Correspondence to Jinpei Liu.

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Liu, J., Xu, Q., Chen, H. et al. Group decision making with interval fuzzy preference relations based on DEA and stochastic simulation. Neural Comput & Applic 31, 3095–3106 (2019). https://doi.org/10.1007/s00521-017-3254-7

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