Neural Network Model for the Risk Prediction in Cold Chain Logistics

This study investigates environment sensitive and perishable products (ESPPs) logistics problem, which is called cold chain logistics problem (CCLs). Based on a comprehensive literature review, we found that there is much room to improve regarding of the risks management in cold chain logistics, that is, the development of a comprehensive cold chain logistics design methodology should considered uncertainty sources and risk exposures. In this study, we propose a neural network model to illustrate the problems. Firstly, the paper develops input indicators at different points in cold chain logistics to examine the effects of environment fluctuations including temperature control, humidity monitoring, the temperature interruption time and electric vehicle mapping, etc; secondly, the improved neural network algorithm can achieve model convergence, including the increase of momentum term, the adjustment of learning rate and the change of error function. At last, through simulation, this study shows that comprehensive risk prediction of cold chain logistics will be calculated based on the input indicators using the improved neural network algorithm, and the predictive value is accurate. So not only the analyzing of kinds of cold chain logistics indicators can be realized through the Neural Network model, but we can take priorities resorting to the predictive results accordingly.