scholarly journals Regional logistics demand forecasting: a BP neural network approach

2021 ◽  
Author(s):  
Lijuan Huang ◽  
Guojie Xie ◽  
Wende Zhao ◽  
Yan Gu ◽  
Yi Huang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Demand Forecasting ◽  
Indicator System ◽  
Regional Logistics ◽  
Logistics Enterprises ◽  
Logistics Demand ◽  
Bp Neural Network Model

AbstractWith 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.

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

2020 ◽  
Vol 11 ◽  
pp. 5
Author(s):  
Ying Chen ◽  
Qiuming Wu ◽  
Lijin Shao
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Fruits And Vegetables ◽  
Prediction Interval ◽  
Added Value ◽  
Cold Chain ◽  
Logistics Demand ◽  
Bp Neural Network Model

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.

Coal Demand Prediction in Shandong Province Based on Artificial Firefly Wavelet Neural Network

2014 ◽  
Vol 962-965 ◽  
pp. 1931-1935
Author(s):  
Jing Hong Yang ◽  
Chang You Wu ◽  
Gui Mei Zhang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Optimization Method ◽  
Wavelet Neural Network ◽  
Systematic Research ◽  
Optimal Solutions ◽  
Demand Forecast ◽  
Demand Prediction ◽  
Firefly Optimization

On the basis of the existing research results, after a systematic research of the wavelet neural network model, we found that the slow convergence and easily get into local optimal solutions. To solve this problem, using artificial firefly optimization method to optimize the parameters in wavelet neural network, and Artificial Firefly Wavelet neural network model is established. Apply this model to the Shandong coal demand forecast achieve better results, proved that establishing artificial Firefly Wavelet neural network model is scientific and feasible.

scholarly journals Forecasting electricity load demand using hybrid exponential smoothing-artificial neural network model

2016 ◽  
Vol 2 (3) ◽  
pp. 131 ◽  
Author(s):  
Winita Sulandari ◽  
Subanar Subanar ◽  
Suhartono Suhartono ◽  
Herni Utami
Keyword(s):  
Neural Network ◽  
Hybrid Model ◽  
Network Model ◽  
Neural Network Model ◽  
Exponential Smoothing ◽  
Series Data ◽  
Demand Forecast ◽  
Forecast Performance ◽  
Load Demand ◽  
Electricity Load

Short-term electricity load demand forecast is a vital requirements for power systems. This research considers the combination of exponential smoothing for double seasonal patterns and neural network model. The linear version of Holt-Winter method is extended to accommodate a second seasonal component. In this work, the Fourier with time varying coefficient is presented as a means of seasonal extraction. The methodological contribution of this paper is to demonstrate how these methods can be adapted to model the time series data with multiple seasonal pattern, correlated non stationary error and nonlinearity components together. The proposed hybrid model is started by implementing exponential smoothing state space model to obtain the level, trend, seasonal and irregular components and then use them as inputs of neural network. Forecasts of future values are then can be obtained by using the hybrid model. The forecast performance was characterized by root mean square error and mean absolute percentage error. The proposed hybrid model is applied to two real load series that are energy consumption in Bawen substation and in Java-Bali area. Comparing with other existing models, results show that the proposed hybrid model generate the most accurate forecast

scholarly journals Traffic demand Forecast of online car-hailing based on BP Neural Network

2020 ◽  
Vol 214 ◽  
pp. 02035
Author(s):  
Junji Jiang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Transport Capacity ◽  
Demand Forecast ◽  
Traffic Demand ◽  
Travel Experience ◽  
Bp Neural Network Model

To accurately predict passengers’ demand for ride-hailing, increase transport capacity in some areas directionally, make it easier for passengers to book ride-hailing, and thus enhance passengers’ travel experience, based on BP neural network model, combined with 2015-Mak-2019 ride-hailing demand change data, and based on MATLAB platform, the demand trend of 20202024 is forecasted. The results show that the demand for shared cars and rides will increase rapidly. Demand for shared bikes and taxis is rising slowly.

Sign in / Sign up

Export Citation Format

Share Document