An improved grey neural network model for predicting transportation disruptions

doi:10.1016/j.eswa.2015.09.052

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	An improved grey neural network model for predicting transportation disruptions
	Liu, Chunxia 1,2; Shu, Tong 1; Chen, Shou 1; Wang, Shouyang1,3 ; Lai, Kin Keung 4,5; Gan, Lu 6
	2016-03-01
发表期刊	EXPERT SYSTEMS WITH APPLICATIONS
ISSN	0957-4174
卷号	45 页码:331-340
摘要	Transportation disruption is the direct result of various accidents in supply chains, which have multiple negative impacts on supply chains and member enterprises. After transportation disruption, market demand becomes highly unpredictable and thus it is necessary for enterprises to better predict market demand and optimize purchase, inventory and production. As such, this article endeavors to design an improved model of grey neural networks to help enterprises better predict market demand after transportation disruption and then the empirical study tests its feasibility. This improved model of grey neural networks exceeds the conventional grey model GM(1,1) with respect to the fact that the raw data tend to show exponential growth and data variation is required to be moderate, demonstrating the good attribute of nonlinear approximation in terms of neural networks, setting up standards for selecting the number of neurons in the input layer of BP neural networks, increasing the fitting degree and prediction accuracy and enhancing the stability and reliability of prediction. It can be applied to sequential data prediction in transportation disruption or mutation, contributing to the prediction of transportation disruption. The forecasting results can provide scientific evidence for demand prediction, inventory management and production of supply chain enterprises. Crown Copyright (C) 2015 Published by Elsevier Ltd. All rights reserved.
关键词	Transportation disruptions GM(1,1) model Neural network Prediction
DOI	10.1016/j.eswa.2015.09.052
语种	英语
资助项目	Natural Science Foundation of China[71172194] ; Natural Science Foundation of China[71390330] ; Natural Science Foundation of China[71390331] ; Natural Science Foundation of China[71221001]
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science
WOS记录号	WOS:000366232700028
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/21489
专题	系统科学研究所
通讯作者	Shu, Tong
作者单位	1.Hunan Univ, Sch Business, Changsha 410082, Hunan, Peoples R China 2.Hunan Univ Finance & Econ, Dept Business Adm, Changsha 410205, Hunan, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100080, Peoples R China 4.Shaanxi Normal Univ, Int Business Sch, Xian 710062, Peoples R China 5.City Univ Hong Kong, Dept Management Sci, Kowloon, Hong Kong, Peoples R China 6.Hunan Univ, Off Humanities & Social Sci, Changsha 410082, Hunan, Peoples R China
推荐引用方式 GB/T 7714	Liu, Chunxia,Shu, Tong,Chen, Shou,et al. An improved grey neural network model for predicting transportation disruptions[J]. EXPERT SYSTEMS WITH APPLICATIONS,2016,45:331-340.
APA	Liu, Chunxia,Shu, Tong,Chen, Shou,Wang, Shouyang,Lai, Kin Keung,&Gan, Lu.(2016).An improved grey neural network model for predicting transportation disruptions.EXPERT SYSTEMS WITH APPLICATIONS,45,331-340.
MLA	Liu, Chunxia,et al."An improved grey neural network model for predicting transportation disruptions".EXPERT SYSTEMS WITH APPLICATIONS 45(2016):331-340.