Title: Application of grey BP neural network in port logistics demand analysis

Authors: Wei Xu; Nan Yu

Addresses: Department of Logistics Engineering, School of Transportation, Shandong University of Science and Technology, Qingdao, Shandong, China ' Department of Logistics Engineering, School of Transportation, Shandong University of Science and Technology, Qingdao, Shandong, China

Abstract: From the perspective of port cargo throughput, this paper firstly analyses the characteristics and influencing factors of port logistics demand. Secondly, considering the characteristics of nonlinear logistics demand and small sample modelling, the modelling adopts GM(1, 1) and the single prediction model of BP neural network for calculation. Then, based on the prediction results and the target of minimum fitting prediction square-error, the single model is given weight, and the combined prediction model is constructed. Finally, taken Qingdao Port as an example, the port logistics demand is simulated by MATLAB software. The results show that the combined forecasting model has higher accuracy and stronger stability than the single forecasting model, which can effectively reduce the error rate and make the forecasting result closer to reality, thus having guiding significance for the future port logistics development planning.

Keywords: BP neural network; port logistics; logistics demand forecast; GM(1, 1).

DOI: 10.1504/IJMOM.2019.103048

International Journal of Modelling in Operations Management, 2019 Vol.7 No.3, pp.249 - 267

Received: 03 Apr 2019
Accepted: 26 Jun 2019
Published online: 14 Oct 2019 *

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