Advanced Hybrid Information Processing. First International Conference, ADHIP 2017, Harbin, China, July 17–18, 2017, Proceedings

Research Article

Trusted Computing Based on Interval Intuitionistic Fuzzy Sets in Cloud Manufacturing

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  • @INPROCEEDINGS{10.1007/978-3-319-73317-3_23,
        author={Xiaolan Xie and Xiaofeng Gu and Xiaochun Cheng},
        title={Trusted Computing Based on Interval Intuitionistic Fuzzy Sets in Cloud Manufacturing},
        proceedings={Advanced Hybrid Information Processing. First International Conference, ADHIP 2017, Harbin, China, July 17--18, 2017, Proceedings},
        proceedings_a={ADHIP},
        year={2018},
        month={2},
        keywords={Cloud manufacturing Trust assessment mechanism Interval intuitionistic fuzzy sets Trust Multi-attribute group decision},
        doi={10.1007/978-3-319-73317-3_23}
    }
    
  • Xiaolan Xie
    Xiaofeng Gu
    Xiaochun Cheng
    Year: 2018
    Trusted Computing Based on Interval Intuitionistic Fuzzy Sets in Cloud Manufacturing
    ADHIP
    Springer
    DOI: 10.1007/978-3-319-73317-3_23
Xiaolan Xie,*, Xiaofeng Gu1,*, Xiaochun Cheng2,*
  • 1: Guilin University of Technology
  • 2: Middlesex University
*Contact email: 237290696@qq.com, gxf199295@163.com, x.cheng@mdx.ac.uk

Abstract

Aiming at the problem that the trust information is not complete in the existing cloud manufacturing and the single model lacks the multi-perspective, the model of the trust evaluation mechanism in the cloud manufacturing environment is established, at the same time, using the interval intuitionistic fuzzy set (IVIFS), this paper proposes a trusted computing model based on interval intuitionistic fuzzy sets in cloud manufacturing. Through experimental analysis, and finally through the results of sorting, to get the optimal solution of trust, which solves the problem that the information in the process of interaction between the demand side and the service side is not complete or the fuzzy uncertainty of the attribute itself is difficult to give the information of accurate preference.