Interruption Risk Assessment and Transmission of Fresh Cold Chain Network Based on a Fuzzy Bayesian Network | Zendy

Huanwan Chen | Zendy; Qingnian Zhang | Zendy; Jing Luo | Zendy; Xiuxia Zhang | Zendy; Guopeng Chen | Zendy

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Interruption Risk Assessment and Transmission of Fresh Cold Chain Network Based on a Fuzzy Bayesian Network

Author(s) -

Huanwan Chen,

Qingnian Zhang,

Jing Luo,

Xiuxia Zhang,

Guopeng Chen

Publication year - 2021

Publication title -

discrete dynamics in nature and society

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.264

H-Index - 39

eISSN - 1607-887X

pISSN - 1026-0226

DOI - 10.1155/2021/9922569

Subject(s) - bayesian network , computer science , transmission (telecommunications) , fuzzy logic , node (physics) , bayesian probability , sensitivity (control systems) , risk assessment , chain (unit) , set (abstract data type) , cold chain , topology (electrical circuits) , mathematics , artificial intelligence , engineering , telecommunications , computer security , mechanical engineering , structural engineering , combinatorics , electronic engineering , programming language , physics , astronomy

The fresh cold chain network is complex, and the interruption risk can significantly impact it. Based on the Bayesian theory, we constructed a fresh cold chain network interruption risk topology structure. The probability of each root node was predicted and calculated based on the fuzzy set theory. The evaluation model was then validated and improved through the virus transmission model based on risk transmission. Sensitivity analysis was used to determine significant risk factors. Several strategies for minimizing interruption risks were identified.

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