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author:

Qiang, R. (Qiang, R..) [1] | Hu, X.-L. (Hu, X.-L..) [2] | Lu, L.-X. (Lu, L.-X..) [3]

Indexed by:

Scopus

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. © 2011 IEEE.

Keyword:

crisis early warning; feature weighting; rough set; Supply chain quality; SVM

Community:

  • [ 1 ] [Qiang, R.]Management Science and Engineering, Fuzhou University, Fujian, China
  • [ 2 ] [Qiang, R.]Fuzhou University, No. 523, Industry Road, Gulou District, Fuzhou, China
  • [ 3 ] [Hu, X.-L.]Management Science and Engineering, Fuzhou University, Fujian, China
  • [ 4 ] [Lu, L.-X.]Management Science and Engineering, Fuzhou University, Fujian, China

Reprint 's Address:

  • [Qiang, R.]Management Science and Engineering, Fuzhou University, Fujian, China

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Source :

2011 IEEE 18th International Conference on Industrial Engineering and Engineering Management, IE and EM 2011

Year: 2011

Issue: PART 2

Page: 1309-1312

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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