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An improved grey neural network model for predicting transportation disruptions
Liu, Chunxia1,2; Shu, Tong1; Chen, Shou1; Wang, Shouyang1,3; Lai, Kin Keung4,5; Gan, Lu6
2016-03-01
Source PublicationEXPERT SYSTEMS WITH APPLICATIONS
ISSN0957-4174
Volume45Pages:331-340
AbstractTransportation 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.
KeywordTransportation disruptions GM(1,1) model Neural network Prediction
DOI10.1016/j.eswa.2015.09.052
Language英语
Funding ProjectNatural Science Foundation of China[71172194] ; Natural Science Foundation of China[71390330] ; Natural Science Foundation of China[71390331] ; Natural Science Foundation of China[71221001]
WOS Research AreaComputer Science ; Engineering ; Operations Research & Management Science
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science
WOS IDWOS:000366232700028
PublisherPERGAMON-ELSEVIER SCIENCE LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/21489
Collection系统科学研究所
Corresponding AuthorShu, Tong
Affiliation1.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
Recommended Citation
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.
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