Abstract
Article history: Received October 1, 2011 Received in Revised form October, 3, 2011 Accepted 10 December 2011 Available online 15 December 2011 Railroad transportation planning is strategically a long term and an important decision making problem especially in the area of travelling passengers. There have been literally various methods to use in order to provide optimum traveling schedule such as direct or indirect methods. Direct solutions involve the implementation of mixed integer programming, which is often hard to solve for real-world applications. The proposed model of this paper uses a column generation method to decompose a large-scale railroad passenger-scheduling problem into some smaller scale problems, which are easier to solve. The primary concern with the resulted problem is that final solutions of the method need to be integer and this is in contrast with convexity assumption of column generation techniques. We propose heuristic method to handle this problem and apply the proposed model for some examples. The preliminary results indicate that the proposed model of this paper could provide optimal solutions for small-scale problems and it can reach some reasonable solutions for larger problems when direct implementation fails to do in reasonable amount of time. © 2012 Growing Science Ltd. All rights reserved.
Highlights
Train scheduling is one of the most important issues in railroad industry
The results indicated that their proposed methodology would be a very useful tool for the real-life train scheduling problems
We have proposed a column generation method to decompose a large-scale railroad passenger-scheduling problem into some smaller scale problems, which are easier to solve
Summary
Train scheduling is one of the most important issues in railroad industry. During the past few decades, there have been tremendous changes in world's railroad industry and it is believed to be one of the most and secure facilities for transporting passengers between cities. They estimated time separation among trains, and modeled the scheduling problem with an alternative graph formulation They developed a branch and bound method, which enhances implication rules enabling to increase the computation. Tsai et al (2009), for instance used Neural network based temporal feature models for short-term railway passenger demand forecasting Another alternative solution to determine the capacity of train scheduling is to use system dynamic methods (Suryani et al, 2010). In their modeling formulation, all trains, single-track sections and multiple-track segments, respectively, are synonymous with jobs, single and parallel machines, and an operation was considered as the movement/traversal of a particular train across a section They solved a parallel-machine job-shop-scheduling (PMJSS) problem using an improved shifting bottleneck procedure (SBP) method without considering blocking conditions for a real-world application from Queensland Rail and analyzed the results. Eq (4) assigns an upper limit on the number of trips and Eq (5) specifies the type of each variable
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