A Multiobjective Optimization for Train Routing at the High-Speed Railway Station Based on Tabu Search Algorithm

Ziyan Feng, Chengxuan Cao, Yutong Liu and Yaling Zhou

Mathematical Problems in Engineering, 2018, vol. 2018, 1-22

Abstract:

This paper focuses on the train routing problem at a high-speed railway station to improve the railway station capacity and operational efficiency. We first describe a node-based railway network by defining the turnout node and the arrival-departure line node for the mathematical formulation. Both considering potential collisions of trains and convenience for passengers’ transfer in the station, the train routing problem at a high-speed railway station is formulated as a multiobjective mixed integer nonlinear programming model, which aims to minimize trains’ departure time deviations and total occupation time of all tracks and keep the most balanced utilization of arrival-departure lines. Since massive decision variables for the large-scale real-life train routing problem exist, a fast heuristic algorithm is proposed based on the tabu search to solve it. Two sets of numerical experiments are implemented to demonstrate the rationality and effectiveness of proposed method: the small-scale case confirms the accuracy of the algorithm; the resulting heuristic proved able to obtain excellent solution quality within 254 seconds of computing time on a standard personal computer for the large-scale station involving up to 17 arrival-departure lines and 46 trains.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:8394397

DOI: 10.1155/2018/8394397

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