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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 8701081, 14 pages
https://doi.org/10.1155/2017/8701081
Research Article

Railway Container Station Reselection Approach and Application: Based on Entropy-Cloud Model

School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, Sichuan 610031, China

Correspondence should be addressed to Wencheng Huang

Received 7 June 2016; Revised 14 November 2016; Accepted 16 November 2016; Published 5 January 2017

Academic Editor: Peide Liu

Copyright © 2017 Wencheng Huang 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.

Abstract

Reasonable railway container freight stations layout means higher transportation efficiency and less transportation cost. To obtain more objective and accurate reselection results, a new entropy-cloud approach is formulated to solve the problem. The approach comprises three phases: Entropy Method is used to obtain the weight of each subcriterion during Phase  1, then cloud model is designed to form the evaluation cloud for each subcriterion during Phase  2, and finally during Phase  3 we use the weight during Phase  1 to multiply the initial evaluation cloud during Phase  2. MATLAB is applied to determine the evaluation figures and help us to make the final alternative decision. To test our approach, the railway container stations in Wuhan Railway Bureau were selected for our case study. The final evaluation result indicates only Xiangyang Station should be renovated and developed as a Special Transaction Station, five other stations should be kept and developed as Ordinary Stations, and the remaining 16 stations should be closed. Furthermore, the results show that, before the site reselection process, the average distance between two railway container stations was only 74.7 km but has improved to 182.6 km after using the approach formulated in this paper.