Integrated Condition-Based Track Maintenance Planning and Crew Scheduling of Railway Networks

Reference

Z. Su, A. Jamshidi, A. Núñez, S. Baldi, and B. De Schutter, "Integrated Condition-Based Track Maintenance Planning and Crew Scheduling of Railway Networks," Transportation Research Part C, vol. 105, pp. 359-384, Aug. 2019.

Abstract

We develop a multi-level decision making approach for optimal condition-based maintenance planning of a railway network divided into a large number of sections with independent stochastic deterioration dynamics. At higher level, a chance-constrained Model Predictive Control (MPC) controller determines the long-term section-wise maintenance plan, minimizing condition deterioration and maintenance costs for a finite planning horizon, while ensuring that the deterioration level of each section stays below the maintenance threshold with a given probabilistic guarantee in the presence of parameter uncertainty. The resulting large MPC optimization problem containing both continuous and discrete decision variables is solved using Dantzig-Wolfe decomposition to improve the scalability of the proposed approach. At a lower level, the optimal short-term scheduling of the maintenance interventions suggested by the high-level controller and the optimal routing of the corresponding maintenance crew is formulated as a capacitated arc routing problem, which is solved exactly by transforming it into a node routing problem. The proposed approach is illustrated by a numerical case study on the optimal treatment of squats of a regional Dutch railway network. Simulation results show that the proposed approach is robust, non-conservative, and scalable.

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Bibtex entry

@article{SuJam:19-018,
author={Z. Su and A. Jamshidi and A. N{\'{u}}{\~{n}}ez and S. Baldi and B. {D}e Schutter},
title={Integrated Condition-Based Track Maintenance Planning and Crew Scheduling of Railway Networks},
journal={Transportation Research Part C},
volume={105},
pages={359--384},
month=aug,
year={2019},
doi={10.1016/j.trc.2019.05.045}
}

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