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Dynamic fleet maintenance management model applied to rolling stock

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  • Crespo del Castillo, Adolfo
  • Marcos, José Antonio
  • Parlikad, Ajith Kumar

Abstract

This paper presents a model for optimising fleet maintenance management with a particular application to train rolling stock fleets. The proposed model produces a joint schedule for train operations and opportunistic predictive maintenance activities with an aim to maximise operational useful life. The model opportunistically allocates predictive maintenance interventions to existing preventive maintenance schedule considering the estimated remaining useful life (RUL) of critical components whilst ensuring fleet availability to meet operational demands as well as resource and time constraints at the maintenance depots. The proposed methodology is described in three phases: (i) definition of the operating context and maintenance resources; (ii) evaluation of feasible opportunistic maintenance timeslots; (iii) optimal maintenance and operations scheduling. The optimisation model, developed as a Mixed Integer Linear Programming problem, is applied to a real industrial case study on a fleet of high-speed trains in Spain. The results show significant improvement in the utilisation of operational life of components compared to the current policies used by the company. Although the model was developed with particular consideration to the train fleets, it can be adapted for other sectors such as bus fleets and airlines with similar operational constraints.

Suggested Citation

  • Crespo del Castillo, Adolfo & Marcos, José Antonio & Parlikad, Ajith Kumar, 2023. "Dynamic fleet maintenance management model applied to rolling stock," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    • Handle: RePEc:eee:reensy:v:240:y:2023:i:c:s0951832023005215
    DOI: 10.1016/j.ress.2023.109607
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    References listed on IDEAS

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    2. Petchrompo, Sanyapong & Li, Hao & Erguido, Asier & Riches, Chris & Parlikad, Ajith Kumar, 2020. "A value-based approach to optimizing long-term maintenance plans for a multi-asset k-out-of-N system," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    3. Petchrompo, Sanyapong & Parlikad, Ajith Kumar, 2019. "A review of asset management literature on multi-asset systems," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 181-201.
    4. de Jonge, Bram & Scarf, Philip A., 2020. "A review on maintenance optimization," European Journal of Operational Research, Elsevier, vol. 285(3), pages 805-824.
    5. de Pater, Ingeborg & Mitici, Mihaela, 2021. "Predictive maintenance for multi-component systems of repairables with Remaining-Useful-Life prognostics and a limited stock of spare components," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    6. Zhou, Hang & Lopes Genez, Thiago Augusto & Brintrup, Alexandra & Parlikad, Ajith Kumar, 2022. "A hybrid-learning decomposition algorithm for competing risk identification within fleets of complex engineering systems," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
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    Cited by:

    1. Zhou, Yu & Zheng, Ran, 2024. "Capacity-based daily maintenance optimization of urban bus with multi-objective failure priority ranking," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    2. Li, Yan & Zhang, Wei & Liu, Baoliang & Wang, Xiaofeng, 2024. "Availability and maintenance strategy under time-varying environments for redundant repairable systems with PH distributions," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    3. Lu, Yaohui & Wang, Shaoping & Zhang, Chao & Chen, Rentong & Dui, Hongyan & Mu, Rui, 2024. "Adaptive maintenance window-based opportunistic maintenance optimization considering operational reliability and cost," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    4. Crespo del Castillo, Adolfo & Parlikad, Ajith Kumar, 2024. "Dynamic fleet management: Integrating predictive and preventive maintenance with operation workload balance to minimise cost," Reliability Engineering and System Safety, Elsevier, vol. 249(C).

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