Risk Assessment of Fresh Cold Chain Logistics Based on Fuzzy DBN

To address problems such as numerous risk factors and time-varying risk states in fresh cold chain logis-tics, a risk assessment method for fresh cold chain logistics is studied based on the fuzzy dynamic Bayesian network (DBN). To improve the efficiency of dynamic risk assessment for fresh cold chain logistics and accurately identify key risk triggers. From the perspective of full-process and multi-dimensional integration, the entire operation pro-cess of fresh cold chain logistics is deconstructed using the decomposition analysis method. Core indicators are screened in depth by combining the EWM and TOPSIS. Through these steps, a full-process and multi-dimensional risk assessment indicator system for fresh cold chain logistics is constructed. Meanwhile, fuzzy theory is integrated, and fresh product characteristic parameters are incorporated to determine the conditional probability distribution of the DBN. Thus, a DBN risk assessment model for fresh cold chain logistics is established. An empirical analysis is conducted with a fresh cold chain logistics enterprise in Wuhan as the research object. The DBN risk assessment model is built using GeNIe software, and the probabilities of risk factors in fresh cold chain logistics are evaluated. Results show that the occurrence probability of fresh cold chain logistics risks increases from 0.24 to 0.31 over time. Among all links, the transportation link exhibits the highest risk occurrence probability and constitutes a key risk variable in the fresh cold chain logistics system. Storage risks are prone to transfer to the transportation link due to improper stacking methods and unsuitable storage temperatures as time passes. This transfer leads to an approxi-mately 10% increase in transportation risks, which exerts the greatest impact on fresh cold chain logistics risks. Compared with the static BN, the accuracy of the fuzzy DBN risk assessment is improved by 19.73%.