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Computational Intelligence and Neuroscience
Volume 2014, Article ID 607159, 6 pages
http://dx.doi.org/10.1155/2014/607159
Research Article

Robust Optimization Model and Algorithm for Railway Freight Center Location Problem in Uncertain Environment

1School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
2Integrated Transport Research Center, China Academy of Transportation Sciences, Beijing 100029, China

Received 11 July 2014; Accepted 5 October 2014; Published 4 November 2014

Academic Editor: Xiaobei Jiang

Copyright © 2014 Xing-cai Liu 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

Railway freight center location problem is an important issue in railway freight transport programming. This paper focuses on the railway freight center location problem in uncertain environment. Seeing that the expected value model ignores the negative influence of disadvantageous scenarios, a robust optimization model was proposed. The robust optimization model takes expected cost and deviation value of the scenarios as the objective. A cloud adaptive clonal selection algorithm (C-ACSA) was presented. It combines adaptive clonal selection algorithm with Cloud Model which can improve the convergence rate. Design of the code and progress of the algorithm were proposed. Result of the example demonstrates the model and algorithm are effective. Compared with the expected value cases, the amount of disadvantageous scenarios in robust model reduces from 163 to 21, which prove the result of robust model is more reliable.