Dynamic fleet maintenance management model applied to rolling stock

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

Reliability Engineering and System Safety, 2023, vol. 240, issue C

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.

Keywords: Dynamic fleet maintenance; Fleet management; Predictive maintenance; Dynamic maintenance scheduling; Maintenance decision making; Digital asset management; Asset management (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:240:y:2023:i:c:s0951832023005215

DOI: 10.1016/j.ress.2023.109607

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