Proceedings of the 13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020, 27-28 August 2020, Cyberspace

Research Article

Research on node importance evaluation of railway logistics distribution network based on Rough Set

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  • @INPROCEEDINGS{10.4108/eai.27-8-2020.2294625,
        author={Xiu-miao  Liu},
        title={Research on node importance evaluation of railway logistics distribution network based on Rough Set },
        proceedings={Proceedings of the 13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020, 27-28 August 2020, Cyberspace},
        publisher={EAI},
        proceedings_a={MOBIMEDIA},
        year={2020},
        month={11},
        keywords={rough set; collection and distribution network; node importance},
        doi={10.4108/eai.27-8-2020.2294625}
    }
    
  • Xiu-miao Liu
    Year: 2020
    Research on node importance evaluation of railway logistics distribution network based on Rough Set
    MOBIMEDIA
    EAI
    DOI: 10.4108/eai.27-8-2020.2294625
Xiu-miao Liu1,*
  • 1: HEBEI VOCATIONAL COLLEGE OF RAIL TRANSPORTATION, Shijiazhuang 050000, China
*Contact email: liuxiumiao22@163.com

Abstract

The traditional evaluation method of the node importance of railway logistics distribution network has not eliminated redundant data, resulting in errors in the evaluation results. Therefore, the evaluation method of the node importance of railway logistics distribution network based on rough set is proposed. This method uses the rough set theory to eliminate redundant node data, obtain the collection and distribution uncertainty index, establish an importance evaluation index, obtain the node importance evaluation weight, and achieve the higher accuracy of the network node importance evaluation. The experimental results show that compared with the traditional evaluation method, the evaluation results of network node importance based on rough set are closer to the expected results. It can be seen that the performance of this method is superior.