MATEC Web Conf.
Volume 119, 2017The Fifth International Multi-Conference on Engineering and Technology Innovation 2016 (IMETI 2016)
|Number of page(s)||9|
|Published online||04 August 2017|
The crisis early warning of the quality of supply chain based on rough set&feature weighted support vector machine
Department of Logistics, Fuzhou University of International Studies and Trade, Fuzhou 350202, Fujian, China
a Corresponding author : email@example.com
A Rough Set&Feature Weighted Support Vector Machine(RS-FWSVM) model is proposed for the quality of supply chain crisis early-warning, which aims at some problems of the quality of supply chain. This model combines the advantages of the RS and FWSVM, which can get classification per-formances by changing the weights of different linear functions in the feature space. 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.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.