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Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 5795604, 8 pages
Research Article

A Soft Rough-Fuzzy Preference Set-Based Evaluation Method for High-Speed Train Operation Diagrams

1School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China
2National Railway Train Diagram Research and Training Center, Southwest Jiaotong University, Chengdu 610031, China
3National and Local Joint Engineering Laboratory of Comprehensive Intelligent Transportation, Chengdu 610031, China
4School of Mathematics, Southwest Jiaotong University, Chengdu 610031, China

Received 11 November 2015; Accepted 31 January 2016

Academic Editor: Mingshu Peng

Copyright © 2016 Dingjun Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper proposes a method of high-speed railway train operation diagram evaluation based on preferences of locomotive operation, track maintenance, S & C, vehicles and other railway departments, and customer preferences. The application of rough set-based attribute reduction obtains the important relative indicators by eliminating excessive and redundant evaluation indicators. Soft fuzzy set theory is introduced for the overall evaluation of train operation diagrams. Each expert utilizes a set of indicators during evaluation based on personal preference. In addition, soft fuzzy set theory is applied to integrate the information obtained via expert evaluation in order to obtain an overall evaluation. The proposed method was validated by a case study. Results demonstrate that the proposed method flexibly expresses the subjective judgments of experts while effectively and reasonably handling the uncertainty of information, which is consistent with the judgment process of humans. The proposed method is also applicable to the evaluation of train operation schemes which consist of multiple diagrams.