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Optimization of maintenance strategies for railway track-bed considering probabilistic degradation models and different reliability levels

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  • Bressi, Sara
  • Santos, João
  • Losa, Massimo

Abstract

An optimization-based maintenance scheduling framework is an essential tool to plan the necessary investment to maintain the required performance of a railway line. In the present study, a methodology is proposed to minimize the present value of the life cycle maintenance costs and maximize the life cycle quality level of the track-bed considering different levels of reliability. Probabilistic degradation models are developed for predicting the evolution of the railway track condition over time. Afterwards, a Genetic Algorithm based optimization procedure is applied for obtaining a set of optimal solutions taking into account several constrains. The proposed methodology is applied to an Italian railway track-line case study. The results show that it is possible to develop a decision support system to help railway managers to schedule railway track maintenance operations based on the optimal trade-off between maintenance costs and railway track geometry condition for different levels of reliability.

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  • Bressi, Sara & Santos, João & Losa, Massimo, 2021. "Optimization of maintenance strategies for railway track-bed considering probabilistic degradation models and different reliability levels," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    • Handle: RePEc:eee:reensy:v:207:y:2021:i:c:s0951832020308498
    DOI: 10.1016/j.ress.2020.107359
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    7. Levitin, Gregory & Xing, Liudong & Dai, Yanshun, 2022. "Minimum cost replacement and maintenance scheduling in dual-dissimilar-unit standby systems," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    8. Chadha, Mayank & Ramancha, Mukesh K. & Vega, Manuel A. & Conte, Joel P. & Todd, Michael D., 2023. "The modeling of risk perception in the use of structural health monitoring information for optimal maintenance decisions," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
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    10. Levitin, Gregory & Xing, Liudong & Dai, Yuanshun, 2022. "Heterogeneous 1-out-of-n standby systems with limited unit operation time," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    11. Lee, Jun S. & Yeo, In-Ho & Bae, Younghoon, 2024. "A stochastic track maintenance scheduling model based on deep reinforcement learning approaches," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    12. Levitin, Gregory & Xing, Liudong & Xiang, Yanping, 2021. "Optimal multiple replacement and maintenance scheduling in two-unit systems," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    13. Andrés Cacereño & David Greiner & Blas J. Galván, 2021. "Multi-Objective Optimum Design and Maintenance of Safety Systems: An In-Depth Comparison Study Including Encoding and Scheduling Aspects with NSGA-II," Mathematics, MDPI, vol. 9(15), pages 1-39, July.
    14. Wang, Dapeng & Qiu, Haobo & Gao, Liang & Jiang, Chen, 2021. "A single-loop Kriging coupled with subset simulation for time-dependent reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    15. Levitin, Gregory & Xing, Liudong & Dai, Yuanshun, 2022. "Optimal loading of repairable system with perfect product storage," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
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