Demand forecasting model of coal logistics based on drosophila-grey neural network

Shudong Wang (), Qinfeng Xing, Xiangqian Wang and Qian Wu
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Shudong Wang: Anhui University of Science and Technology
Qinfeng Xing: Anhui University of Science and Technology
Xiangqian Wang: Anhui University of Science and Technology
Qian Wu: Anhui University of Science and Technology

International Journal of System Assurance Engineering and Management, 2023, vol. 14, issue 2, No 31, 807-815

Abstract: Abstract The demanding forecast of coal logistics is the premise of integrating coal logistics resources and improving its efficiency. First, we considered the principles of index system construction and many other factors and chose GDP, average consumption, value added of the secondary industry, coal import and export volume, urban population quantity, coal production, total energy consumption, average annual rain PH, and coal consumption as a measure of indicators. Then, based on the idea of a combination model, the combination of grey model and BP neural network is selected, and FOA-GNNM is used to predict the coal consumption in China from 2018 to 2022, and the coal transportation volume in railway, highway and waterway is further calculated. The findings can help for better understanding of coal logistics, and then the following suggestions are proposed to improve its demanding forecast for China's future coal logistics operation arrangements, route planning and reserve system construction.

Keywords: Coal logistics; Demand forecasting; Fruit fly algorithm; Grey model; BP neural network (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s13198-021-01586-x

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