Prescriptive Maintenance through Workload Allocation for Synchronizing Parallel Machines
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Abstract
This paper addresses workload allocation for parallel machines subject to stochastic degradation in order to improve maintenance synchronization. In such systems, maintenance actions are often performed simultaneously across machines, which creates a trade off between premature maintenance and unexpected failures. Existing workload allocation strategies mainly focus on reducing failure probability or balancing degradation but do not explicitly aim at synchronizing maintenance conditions.
To address this issue, this paper proposes a framework combining stochastic degradation modeling and workload optimization. Machine degradation is modeled using a Gamma process in which the degradation rate depends on the assigned workload. A Sequential Quadratic Programming optimization is used to dynamically allocate workloads in order to maximize the probability that all machines reach the maintenance window defined by preventive and failure thresholds at the same time. Remaining Useful Life estimates are used to trigger workload reallocations and maintenance decisions.
Monte Carlo simulations compare the proposed strategy with a deterioration based workload allocation method from the literature. The results show that the proposed approach maintains a low proportion of corrective maintenance while achieving a high proportion of preventive maintenance with fewer workload reallocations.
How to Cite
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Prescriptive Maintenance, Maintenance synchronization, stochastic degradation, parallel machines
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