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
@article{SuJam:19-018,
author = {Su, Zhou and Jamshidi, Ali and N{\'{u}}{\~{n}}ez, Alfredo and
Baldi, Simone and De Schutter, Bart},
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}
}