Reference
Z. Su, A. Jamshidi, A. Núñez, S. Baldi, and B. De Schutter,
"Multi-level condition-based maintenance planning for railway infrastructures -
A scenario-based chance-constrained approach,"
Transportation
Research Part C, vol. 84, pp. 92-123, Nov. 2017.
Abstract
This paper develops a multi-level decision making approach for the optimal
planning of maintenance operations of railway infrastructures, which are
composed of multiple components divided into basic units for maintenance.
Scenario-based chance-constrained Model Predictive Control (MPC) is used at the
high level to determine an optimal long-term component-wise intervention plan
for a railway infrastructure, and the Time Instant Optimization (TIO) approach
is applied to transform the MPC optimization problem with both continuous and
integer decision variables into a nonlinear continuous optimization problem.
The middle-level problem determines the allocation of time slots for the
maintenance interventions suggested at the high level to optimize the trade-off
between traffic disruption and the setup cost of maintenance slots. Based on
the high-level intervention plan, the low-level problem determines the optimal
clustering of the basic units to be treated by a maintenance agent, subject to
the time limit imposed by the maintenance slots. The proposed approach is
applied to the optimal treatment of squats, with real data from the
Eindhoven-Weert line in the Dutch railway network.
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BibTeX
@article{SuJam:17-014,
author = {Su, Zhou and Jamshidi, Ali and N{\'{u}}{\~{n}}ez, Alfredo and
Baldi, Simone and De Schutter, Bart},
title = {Multi-Level Condition-Based Maintenance Planning for Railway
Infrastructures -- {A} Scenario-Based Chance-Constrained
Approach},
journal = {Transportation Research Part C},
volume = {84},
pages = {92--123},
month = nov,
year = {2017}
}