Application of SVM Combined with Mackov Chain for Inventory Prediction in Supply Chain

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dc.contributor.author Wang, J
dc.contributor.author Zhu, W
dc.contributor.author Sun, D
dc.contributor.author Lu, H
dc.contributor.editor Hou, T
dc.contributor.editor Mendlinger, S
dc.date.accessioned 2010-05-28T10:00:48Z
dc.date.issued 2008-01
dc.identifier.citation Proceedings of the 4th International Conference on Wireless Communications, Networking, and Mobile Computing, 2008, pp. 1 - 4
dc.identifier.isbn 978-1-4244-2107-7
dc.identifier.other E1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/10957
dc.description.abstract The aim of this paper is to predict the inventory of the relevant upstream enterprises in supply chain. The support vector machine, a novel artificial intelligence-based method developed from statistical learning theory, is adopted herein to establish a short-term stage forecasting model. However, take the fact into account that demand signal is affected by variant random factors and behaves big uncertainty, the predicted accuracy of SVM is not approving when the data show great randomness. It is obligatory that we present Markov chain to improve the predicted accuracy of SVM. This combined model takes advantage of the high predictable power of SVM model and at the same time take advantage of the prediction power of Markov chain modeling on the discrete states based on the SVM modeling residual sequence. Then we use the statistical data of the output of the gasoline of China from Feb-06 to Dec-07 for a validation of the effectiveness of the above model.
dc.publisher IEEE
dc.relation.isbasedon 10.1109/WiCom.2008.1543
dc.subject SVM, supply chain, Markov chain, predict
dc.subject SVM, supply chain, Markov chain, predict
dc.title Application of SVM Combined with Mackov Chain for Inventory Prediction in Supply Chain
dc.type Conference Proceeding
dc.parent Proceedings of the 4th International Conference on Wireless Communications, Networking, and Mobile Computing
dc.journal.number en_US
dc.publocation Piscataway, USA en_US
dc.publocation Piscataway, USA
dc.publocation Piscataway, USA
dc.publocation Piscataway, USA
dc.identifier.startpage 1 en_US
dc.identifier.endpage 4 en_US
dc.cauo.name FEIT.School of Software en_US
dc.conference Verified OK en_US
dc.conference International Conference on Wireless Communications, Networking and Mobile Computing
dc.conference International Conference on Wireless Communications, Networking and Mobile Computing
dc.conference International Conference on Wireless Communications, Networking and Mobile Computing
dc.for 0801 Artificial Intelligence and Image Processing
dc.personcode 0000047860 en_US
dc.personcode 0000047858 en_US
dc.personcode 0000047859 en_US
dc.personcode 000516 en_US
dc.percentage 100 en_US
dc.classification.name Distributed Computing en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom International Conference on Wireless Communications, Networking and Mobile Computing en_US
dc.date.activity 20081012 en_US
dc.date.activity 2008-10-12
dc.date.activity 2008-10-12
dc.date.activity 2008-10-12
dc.location.activity Dalian, China en_US
dc.location.activity Dalian, China
dc.location.activity Dalian, China
dc.location.activity Dalian, China
dc.description.keywords SVM, supply chain, Markov chain, predict en_US
dc.description.keywords SVM, supply chain, Markov chain, predict
dc.description.keywords SVM, supply chain, Markov chain, predict
dc.description.keywords SVM, supply chain, Markov chain, predict
dc.staffid en_US
dc.staffid 000516 en_US
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group /University of Technology Sydney/Strength - Quantum Computation and Intelligent Systems


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