Multi-level condition-based maintenance planning for railway infrastructures – A scenario-based chance-constrained approach
mode - rail, place - europe, infrastructure - maintainance
Model predictive control, Condition-based maintenance, Railway infrastructure, Time-instant optimization, Chance-constrained optimization
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.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Su, Z., Jamshidi, A., Núñez, A., Baldi, S., & De Schutter, B. (2017). Multi-level condition-based maintenance planning for railway infrastructures – A scenario-based chance-constrained approach. Transportation Research Part C: Emerging Technologies, Vol. 84, pp. 92-123.
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