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Mathematical Problems in Engineering
Volume 2012, Article ID 602153, 13 pages
Research Article

Study on the Stochastic Chance-Constrained Fuzzy Programming Model and Algorithm for Wagon Flow Scheduling in Railway Bureau

Traffic and Transportation School, Lanzhou Jiaotong University, Lanzhou 730070, China

Received 15 May 2012; Revised 13 August 2012; Accepted 15 August 2012

Academic Editor: Wuhong Wang

Copyright © 2012 Bin Liu. 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.


The wagon flow scheduling plays a very important role in transportation activities in railway bureau. However, it is difficult to implement in the actual decision-making process of wagon flow scheduling that compiled under certain environment, because of the interferences of uncertain information, such as train arrival time, train classify time, train assemble time, and flexible train-size limitation. Based on existing research results, considering the stochasticity of all kinds of train operation time and fuzziness of train-size limitation of the departure train, aimed at maximizing the satisfaction of departure train-size limitation and minimizing the wagon residence time at railway station, a stochastic chance-constrained fuzzy multiobjective model for flexible wagon flow scheduling problem is established in this paper. Moreover, a hybrid intelligent algorithm based on ant colony optimization (ACO) and genetic algorithm (GA) is also provided to solve this model. Finally, the rationality and effectiveness of the model and algorithm are verified through a numerical example, and the results prove that the accuracy of the train work plan could be improved by the model and algorithm; consequently, it has a good robustness and operability.