Urban cold-chain logistics demand predicting model based on improved neural network model

Ying Chen,Lijin Shao,Qiuming Wu

doi:10.1051/ijmqe/2020003

Abstract

With the popularity of the Internet and mobile terminals, the development of e-commerce has become hotter. Therefore, e-commerce research starts to focus on the statistics and prediction of the cargo volume of logistics. This study briefly introduced the back-propagation (BP) neural network model and principal component analysis (PCA) method and combined them to obtain an improved PCA-BP neural network model. Then the traditional BP neural network model and the improved PCA-BP neural network model were used to perform the empirical analysis of the cold chain logistics demand of fruits and vegetables in city A from 2010 to 2018. The results showed that the main factors that affected the local cold chain logistics demand were the growth rate of GDP, the added value of primary industry, the planting area of fruits and vegetables, and the consumption price index of fruits and vegetables; both kinds of neural networks model could effectively predict the cold chain logistics demand, but the predicted value of the PCA-BP neural network model was more fitted with the actual value. The prediction error of the BP neural network model was larger, and the fluctuation was obvious within the prediction interval. Moreover, the time required for the prediction by the PCA-BP neural network model was less than that by the BP neural network model. In summary, the improved PCA-BP neural network model is faster and more accurate than the traditional BP model in predicting the cold chain logistics demand.

Highlights

With the popularity of the Internet and mobile terminals, e-commerce has developed rapidly
Respectively; the eigenvalues of influence factors X5 ∼ X9 were 0.089, 0.042, 0.019, 0.007, and 0.001 respectively, and their rate of variance contribution were 0.98, 0.45, 0.20, 0.06, and 0.01% respectively, among which the rates of variance contribution of influence factors X1 ∼ X4 were above 1%, and the rate of cumulative variance contribution of the four influence factors was 98.30%, which was larger than 95%; the four influence factors X1 ∼ X4 could be chosen to represent information for the entire sample and input into the principal component analysis (PCA)-BP neural network model
It could be seen that the PCABP neural network model was more accurate than the BP neural network model in predicting demand for the cold chain logistics of fruits and vegetables

Summary

Introduction

With the popularity of the Internet and mobile terminals, e-commerce has developed rapidly. People’s living standards have improved, and the demand for food begin to diversify with the development of the economy [1]. As well as seafood, which require transportation with cryopreservation, have gradually become part of goods in e-commerce. For e-commerce companies, the prediction of logistics demand is an important basis for strategic decision-making and market analysis [2], especially for fruits, vegetables and seafood which require cold chain logistics. Are limited by their own expiration date and cannot be stored as long as other products. Once the judgment of cold chain logistics is wrong, it will cause huge losses. The prediction of the demand for products

Results

Discussion

Conclusion