Scheduling freight trains traveling on complex networks

In the US, freight railways are one of the major means to transport goods from ports to inland destinations. According to the Association of American Railroad’s study, rail companies move more than 40% of the nation’s total freight. Given the fact that the freight railway industry is already running without much excess capacity, better planning and scheduling tools are needed to effectively manage the scarce resources, in order to cope with the rapidly increasing demand for railway transportation. This research develops optimization-based approaches for scheduling of freight trains. Two mathematical formulations of the scheduling problem are first introduced. One assumes the path of each train, which is the track segments each train uses, is given and the other one relaxes this assumption. Several heuristics based on mixtures of the two formulations are proposed. The proposed algorithms are able to outperform two existing heuristics, namely a simple look-ahead greedy heuristic and a global neighborhood search algorithm, in terms of railway total train delay. For large networks, two algorithms based on the idea of decomposition are developed and are shown to significantly outperform two existing algorithms.

► Two optimization based algorithms are first proposed to solve the train scheduling problem for moderate size rail networks.
► The proposed LtdFlePath and GA + FixedPath algorithms are able to outperform two existing heuristics, PureGA and Greedy algorithm, in terms of railway total train delay.
► And the CPU solution times of the proposed heuristics are within a reasonable time constraint.
► Then two decomposition based heuristics, the Decomp and Parallel algorithm, are developed to solve the train scheduling problems for larger networks.
► Both algorithms significantly outperform two existing algorithms.