Logistic Demands and Forecasting of Agriculture Cold Chain Serving Beijing, Tianjin and Hebei Province

Understanding the demands on the logistics of an agricultural product cold chain is essential for appropriate planning, investment, and development of the system, which is unique and complex in operation. A specifically designed program is needed to accurately forecast the demands for an adequate and effective system management. This study applied a qualitative analysis and evaluated with statistical data on factors that might affect the logistics. Subsequently, forecasting models based on the grey model, support vector machine, BP neural network, RBF neural network, and genetic neural network were constructed. By challenging the models on the ability to characterize and correlate the variables as well as predict the outcomes, the following ranking was obtained:genetic neural network > RBF neural network > BP neural network > support vector machine > gray model.