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Optimization of Cargo Shipping Adaptability Modeling Evaluation Based on Bayesian Network Algorithm

Author

Listed:
  • Siyuan Gao

    (School of Economics and Management, Northeast Normal University, Changchun 130024, China
    Faculty of Education, Northeast Normal University, Changchun 130024, China)

  • Fengrong Zhang

    (Faculty of Marxism, Northeast Normal University, Changchun 130024, China)

  • Wei Ning

    (Economic Research Institute of Jilin Province Development and Reform Commission, Changchun 130051, China)

  • Dayong Wu

    (Texas A&M Transportation Institute, Texas A&M University, College Station, TX 77843, USA)

Abstract

Through shipping service adaptability measurement, selecting shipping services that are more adaptable to preferences such as low cost, high efficiency, safety, and obvious emission reduction can achieve synergistic optimization of green shipping management. The study takes green shipping service adaptability as the research theme; explores three aspects, i.e., shipping safety, shipping rate and shipping choice preference, related to the evaluation and selection of a green shipping service; constructs the green shipping service adaptability evaluation index system including safety index, freight rate index and choice preference index; and applies fuzzy-exact by processing the historical data from H shipping company in Hainan Province, China. Bayesian net is applied to calculate the shipping safety adaptation degree of the transportation object. The theory of shipping service adaptability proposed in the paper can be applied to the fields of shipping supplier selection and shipping company’s detection of shipping object status. The fuzzy-exact Bayesian network method chosen in the paper can solve the problem of incomplete state coverage of the Bayesian network and correct the situation that some edge probabilities are unreasonable.

Suggested Citation

  • Siyuan Gao & Fengrong Zhang & Wei Ning & Dayong Wu, 2022. "Optimization of Cargo Shipping Adaptability Modeling Evaluation Based on Bayesian Network Algorithm," Sustainability, MDPI, vol. 14(19), pages 1-25, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12856-:d:936821
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    References listed on IDEAS

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