Research on Logistics Cost Prediction and Control Based on Bayesian Network Model
Accurately predicting logistics costs is the key to improving logistics distribution efficiency,while traditional logistics enterprises lack prediction of logistics transportation costs,which further leads to an increase in logistics costs and low revenue for enterprises.To further improve logistics cost prediction and control,a Bayesian network model is proposed,and a logistics cost prediction model is established by combining local weighted regression methods.Classify the variables between logistics cost characteristics and use maximum likelihood estimation to improve the cost data classification efficiency of Bayesian network models.The experimental results show that the curve error between the logistics cost prediction value and the actual cost value using the Bayesian network model is small,with a minimum error value of only 0.68%.The difference between the actual transportation cost and the predicted cost is only 1 yuan,further indicating that the Bayesian network model can accurately predict logistics costs.The average absolute percentage error and root mean square error in the predictive evaluation indicators are both decreasing,with the minimum values being 2.21%and 3.62,respectively.The confidence level of the Bayesian network model prediction model is set to 80%-85%,which has high cost prediction reliability.
万方数据
维普
