Multi-level condition-based maintenance planning for railway infrastructures – A scenario-based chance-constrained approach

Zhou Su*, Ali Jamshidi, Alfredo Núñez, Simone Baldi, Bart De Schutter

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

41 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)92-123
JournalTransportation Research. Part C: Emerging Technologies
Publication statusPublished - 2017


  • Chance-constrained optimization
  • Condition-based maintenance
  • Model predictive control
  • Railway infrastructure
  • Time-instant optimization


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