Multi-Level Condition-Based Maintenance Planning for Railway Infrastructures - A Scenario-Based Chance-Constrained Approach

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 entry

@article{SuJam:17-014,
author={Z. Su and A. Jamshidi and A. N{\'{u}}{\~{n}}ez and S. Baldi and B. {D}e Schutter},
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},
doi={10.1016/j.trc.2017.08.018}
}

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