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The steady development of China’s economy has led to the rapid development of the logistics industry. Nowadays, the logistics efficiency in the world has been at a high position, but compared with advanced developed countries, logistics costs are still higher. Establishing an effective logistics demand forecasting model is of great significance to...
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Context 1
... uses the reverse-propagation artificial neural network to construct nonlinear functions. The simple multi-layer perceptron is shown in figure 1. doi:10.1088/1742-6596/1288/1/012055 3 Forward transmission as shown in figure 1, and are input layer neurons, , , , are hidden layer neurons, and is output layer neuron, represents the input from the upper layer of the neuron, represents the weight from to , and represents the weight from to , represents ,then the output neuron of each node can be calculated: , , ...
Context 2
... uses the reverse-propagation artificial neural network to construct nonlinear functions. The simple multi-layer perceptron is shown in figure 1. doi:10.1088/1742-6596/1288/1/012055 3 Forward transmission as shown in figure 1, and are input layer neurons, , , , are hidden layer neurons, and is output layer neuron, represents the input from the upper layer of the neuron, represents the weight from to , and represents the weight from to , represents ,then the output neuron of each node can be calculated: , , ...
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Citations
... Han et al. [28] took GDP, total cost of social logistics, investment in social fixed assets, import, and export volume as indicators of logistics demand calculation and prediction. Du and Chen [29] used GDP, post and telecommunications services, total retail sales of social consumer goods, and residents' consumption level as indicators for logistics demand forecasting. The research of the above scholars has a good reference value, which this paper uses to construct the indicator system of logistics demand prediction for e-commerce development in Guangdong province. ...
... Du and Chen [29] developed as shown in Fig. 1. The research framework includes the following seven steps: ...
... In addition, using the indicators of domestic and foreign scholars such as Ishfaq and Sox [15], Hsiao and Hansen [16], Hsu and Wang [17], Fan and Wu [27], Han et al. [28], Du and Chen [29], etc., this study constructed a target layer using the indicators of Guangdong logistics demand prediction. Taking logistics demand environment, commercial trade environment, basic support environment and e-commerce information environment as latent variables, the indicator system takes 13 indicators such as logistics demand scale as observation variables as summarized in Table 2. ...
With the rapid development of e-commerce, the backlog of distribution orders, insufficient logistics capacity and other issues are becoming more and more serious. It is very significant for e-commerce platforms and logistics enterprises to clarify the demand of logistics. To meet this need, a forecasting indicator system of Guangdong logistics demand was constructed from the perspective of e-commerce. The GM (1, 1) model and Back Propagation (BP) neural network model were used to simulate and forecast the logistics demand of Guangdong province from 2000 to 2019. The results show that the Guangdong logistics demand forecasting indicator system has good applicability. Compared with the GM (1, 1) model, the BP neural network model has smaller prediction error and more stable prediction results. Based on the results of the study, it is the recommendation of the authors that e-commerce platforms and logistics enterprises should pay attention to the prediction of regional logistics demand, choose scientific forecasting methods, and encourage the implementation of new distribution modes.
