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     Research Journal of Applied Sciences, Engineering and Technology

    Abstract
2013(Vol.5, Issue:05)
Article Information:

The Analysis of Shunting Locomotives’ Operating Efficiency Based on Gray-DEA

Yunli Zhang, Dingyou Lei, Xiamiao Li and Yanbing Fu
Corresponding Author:  Yanbing Fu 
Submitted: July 27, 2012
Accepted: September 03, 2012
Published: February 11, 2013
Abstract:
In order to explore the configuration of the railway freight shunting locomotives, improve the efficiency of locomotive operation; a DEA cross evaluating method based on grey incidence analysis is presented in this paper. This method is based on collecting underlying data of factors that influence the shunting operation efficiency in train service depot, which utilizes grey incidence analysis to establish corresponding evaluation indicators system. Furthermore, the method as well adopts DEA and DEA cross efficiency model to analyze, evaluate and estimate the efficiency of shunting locomotives operation in train service depots. A sorting result of the efficiency of shunting locomotives operation in each train service depot has been acquired with help of programming using MATLAB 7.0. What the result indicates is that the DEA cross efficiency evaluating model based on grey incidence analysis can reflect the actual state of the efficiency of shunting locomotives operation in each train service depot. Eventually, based on a projection analysis of non-DEA's three decision-making units, key factors that influence the efficiency of shunting locomotives operation are identified, which can provide decision support for further improvement of the configuration and operation in train service depots.

Key words:  Cross evaluation, DEA, efficiency of shunting locomotives operation, grey incidence analysis, , ,
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Cite this Reference:
Yunli Zhang, Dingyou Lei, Xiamiao Li and Yanbing Fu, . The Analysis of Shunting Locomotives’ Operating Efficiency Based on Gray-DEA. Research Journal of Applied Sciences, Engineering and Technology, (05): 1720-1725.
ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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