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The optimization of fleet maintenance management is of utmost importance to ensure the efficient and reliable operation of asset fleets. Traditional maintenance strategies are often reactive or rely on predetermined schedules, which can lead to inefficient resource allocation and increased operational costs. The advent of digital technologies has allowed the integration of predictive maintenance into fleet management. This paradigm shift towards a more data-driven approach enables fleet management to dynamically respond to issues identified through sensors and algorithms that detect anomalies and provide prognostic insights regarding the remaining useful life of components. However, a notable deficiency exists in the integration of predictive maintenance with calendar-based preventive maintenance and fleet operational allocation, thereby impeding value creation for businesses. This paper presents an optimisation model to address this issue by incorporating preventive and predictive maintenance while simultaneously striving to balance the workload to meet operational demand and mitigate potential penalties resulting from failure to meet these demands. The paper also examines the role of component criticality as well as the precision of the RUL (Remaining Useful Life) prognosis. Through the experiments conducted, it has been demonstrated that the allocation of the fleet is subject to change depending on the level of criticality of monitored components. These findings reveal the potential risks and penalties that could arise from an insufficient definition of failure impact severity when integrating predictive maintenance with existing preventive approaches and operational workload balance. Additionally, the experiments underscore the impact of RUL distributions precision on total operating costs, showing the confidence intervals through a sensitivity analysis.
Castillo et al. (Wed,) studied this question.