Demand Prediction of Cold Chain Logistics Under B2C E-Commerce Model
Shen-Xiang Wang and Cheng-Yan Wei
Logistics Department, Guangzhou College of Technology and Business
Guangzhou, Guangdong 510850, China
In order to meet the increasing demand, the demand of cold chain logistics under the background of B2C e-commerce mode is predicted, to provide theoretical guidance for the development of cold chain logistics. A multivariate linear regression demand prediction model based on grey relational analysis is proposed. The present situation of cold chain logistics demand is as the basis for the analysis. Using appropriate quantitative analysis method, the factors affecting the demand of cold chain logistics are screened, and the selection principles of logistics demand evaluation index for cold chain products are determined, including product supply, logistics demand scale, and cold chain efficiency and so on. The grey correlation analysis is used to standardize the data sequence and calculate the correlation degree between the factors. The factor of large correlation degree is chosen as the key factor, and the multivariate linear regression prediction equation is constructed. According to the progressive regression idea, the model is amended to improve the goodness of fit of the model. The grey multivariate regression model is applied to predict and analyze the cold chain logistics demand of a fruit product in a certain city. The result shows that the model can predict the demand of cold chain logistics accurately.
-  J. Ling, M. Jun, and Z. Yang, “Customer-perceived value and loyalty: how do key service quality dimensions matter in the context of B2C e-commerce?,” Service Business, Vol.10, No.2, pp. 301-317, 2016.
-  L. Kan, X. Luo, and L. Zhang, “Evaluation of China’s B2C E-commerce Website: An Analysis of Factors that Influence Online Buying Decision,” Int. J. of Multimedia and Ubiquitous Engineering, Vol. 11, No.3, pp. 143-156, 2016.
-  G. Yang, Y. Zhang, K. Yang et al., “Automated classification of brain images using wavelet-energy and biogeography-based optimization,” Multimedia Tools & Applications, Vol.75, No.23, pp. 15601-15617, 2016.
-  B. Ya, “Study of Food Cold Chain Logistics Demand Forecast Based on Multiple Regression and AW-BP Forecasting Method on System Order Parameters,” J. of Computational and Theoretical Nanoscience, Vol.13, No.7, pp. 4019-4024, 2016.
-  G. Yang, X. Deng, and C. Liu, “Facial expression recognition model based on deep spatiotemporal convolutional neural networks,” Zhongnan Daxue Xuebao (Ziran Kexue Ban)/J. of Central South University (Science and Technology), Vol.47, No.7, pp. 2311-2319, 2016.
-  J. F. Li, X. G. Tuo, Y. Liu et al., “Study on Vehicle Routing Problems in Logistics Distribution,” Computer Simulation, Vol.33, pp. 184-188, 2016.
-  S. Ren, H. L. Chan, and P. Ram, “A Comparative Study on Fashion Demand Forecasting Models with Multiple Sources of Uncertainty,” Annals of Operations Research, Vol.257, No.1-2, pp. 335-355, 2017.
-  Z. Zhu, J. Peng, Z. Zhou, X. Zhang, and Z. Huang, “PSO-SVR-Based Resource Demand Prediction in Cloud Computing,” J. Adv. Comput. Intell. Intell., Vol.20, No.2, pp. 324-331, 2016.
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