KMS Of Academy of mathematics and systems sciences, CAS
| 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
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| 2016-03-01 | |
| Source Publication | EXPERT SYSTEMS WITH APPLICATIONS
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| ISSN | 0957-4174 |
| Volume | 45Pages:331-340 |
| Abstract | 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. |
| Keyword | Transportation disruptions GM(1,1) model Neural network Prediction |
| DOI | 10.1016/j.eswa.2015.09.052 |
| Language | 英语 |
| Funding Project | 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 Research Area | Computer Science ; Engineering ; Operations Research & Management Science |
| WOS Subject | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science |
| WOS ID | WOS:000366232700028 |
| Publisher | PERGAMON-ELSEVIER SCIENCE LTD |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/21489 |
| Collection | 系统科学研究所 |
| Corresponding Author | Shu, Tong |
| Affiliation | 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 |
| 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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