Abstract
Logistics demand forecasting is a prerequisite and an important part of logistics system planning and optimization, especially in emergencies, where short-term, massive and multi-discipline material demands put forward extremely high requirements on the guarantee capacity of logistics systems. In this paper, a logistics demand prediction model based on time series is constructed for the logistics demand characteristics of emergency events. Since the BP neural network method has the advantages of non-linear mapping capability, self-learning and self-adaptive capability, the BP neural network method is used to solve the model, and finally the model is verified and improved by practical cases. The results show that the model and method used in this study can better predict the logistics demand under unexpected events, which meets the need for rapid prediction of logistics demand in the early stage of unexpected events and is of great significance to improve the efficiency of logistics under unexpected events.
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References
Liu Hui, F.: The Research on IPO with BP Neural Network. Wuhan University of Technology, China (2008)
Zhao Minglan, F.: A study on IPO pricing of GEM listed companies based on BP neural network. Lanzhou J. 77–80 (2010)
Wen Ke, F.: Investment bank risk prediction model of dynamic parameters of the neural
Network. Sci. Technol. Bull. 31(09), 192–195 (2015)
Yang Limin, F.: Investment banks risk early warning based on BP neural network. J. Anhui Univ. Technol. (Nat. Sci. Edn.) 96–100 (2006)
Zhang Guozheng, F.: Research on early warning of venture capital risk based on neural network. Sci. Technol. Manag. Res. 182–184 (2006)
Hu Yanjing, F.: BP artificial neural network model: a new visual angle of the financial risk early-warning. J. Chongqing Technol. Bus. Univ. (West. Econ. Forum), 68–71 (2003)
Guo Peng, F.: Research of BOT project risk assessment based on BP neural network. Sci. Technol. Manag. Res. 210–214 (2015)
Li, Haitang, F.: Research on grain pile temperature prediction model based on improved BP neural network. Henan University of Technology, China (2019)
Ye Fei, F.: Prediction Method of Silicon Content in Blast Furnace Hot Metal Based on Improved BP Neural Network. Anhui University of Technology, China (2019)
Song Bo, F.: Clinical path optimization based on BP neural network. Comput. Technol. Dev. 30(04), 156–160 (2020)
Huang Xiaolong, F.: Study on passenger flow prediction of intercity passenger line based on improved BP neural network. Harbin Institute of Technology, China (2019)
Zhao Fanghui, F.: Research on Housing Demand Forecast Based on PSO-BP Neural Network in Hefei City. Hebei University of Engineering, China (2020)
Fan Rui, F.: Research on Demand Prediction of Large Earthquake Emergency Materials Based on PSO-BP Neural Network. Beijing Jiaotong University, China (2020)
Kang Lijun, F.: Application of Particle Swarm Optimization BP Neural Network in Emergency Material Demand Forecasting. Lanzhou Jiaotong University, China (2013)
Acknowledgement
Supported by the National Key Research and Development Program of China (Grant No. 2018YFB1403101) and the Fundamental Research Funds for the Central Universities (Grant No. 2020RC22).
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Ming, K., Ying, Z., Jing, Z. (2021). Research on the Prediction of Logistics Demand for Emergencies Based on BP Neural Network. In: Cao, W., Ozcan, A., Xie, H., Guan, B. (eds) Computing and Data Science. CONF-CDS 2021. Communications in Computer and Information Science, vol 1513. Springer, Singapore. https://doi.org/10.1007/978-981-16-8885-0_27
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DOI: https://doi.org/10.1007/978-981-16-8885-0_27
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