Title :
The research into crisis early warning of supply chain quality based on Rough Set&Feature Weighted Support Vector Machine
Author :
Qiang, Rui ; Hu, Xiu-Lian ; Lu, Li-Xia
Author_Institution :
Manage. Sci. & Eng., Fuzhou Univ., Fuzhou, China
Abstract :
A RS-FWSVM model is presented by means of combining RS (Rough Set) with FWSVM (Feature Weighted Support Vector Machine) theory. Application process of this model to the crisis early warning of SCQ is researched, which can help enable chain enterprises to identify crises in the process of operations and to predict possible crises.
Keywords :
economic cycles; rough set theory; supply chain management; support vector machines; RS-FWSVM model; chain enterprises; crisis early warning; feature weighted support vector machine theory; rough set theory; supply chain quality; Fires; Indexes; Kernel; Supply chains; Support vector machines; SVM; Supply chain quality; crisis early warning; feature weighting; rough set;
Conference_Titel :
Industrial Engineering and Engineering Management (IE&EM), 2011 IEEE 18Th International Conference on
Conference_Location :
Changchun
Print_ISBN :
978-1-61284-446-6
DOI :
10.1109/ICIEEM.2011.6035396
