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Integrated condition-based track maintenance planning and crew scheduling of railway networks.
- Source :
-
Transportation Research Part C: Emerging Technologies . Aug2019, Vol. 105, p359-384. 26p. - Publication Year :
- 2019
-
Abstract
- • A methodology for condition-based maintenance planning and crew scheduling for railway track. • A distributed optimization scheme to apply the proposed approach to large-scale railway networks. • A chance-constrained formulation to achieve a robust but conservative maintenance plan. 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 0968090X
- Volume :
- 105
- Database :
- Academic Search Index
- Journal :
- Transportation Research Part C: Emerging Technologies
- Publication Type :
- Academic Journal
- Accession number :
- 137826184
- Full Text :
- https://doi.org/10.1016/j.trc.2019.05.045