Aircraft engine maintenance, repair, and overhaul (MRO) exemplifies a closed-loop remanufacturing system in which all components are recovered. As a critical process ensuring aircraft safety and reliability, MRO faces significant challenges due to the inherent uncertainty in maintenance workloads and the stochastic nature of the process. Aircraft engines contain life-limited parts, replaced at predetermined intervals, and on-condition parts, which are inspected during each maintenance visit and replaced as needed. The presence of on-condition components introduces additional uncertainty, as the full scope of required maintenance is only known after disassembly and inspection.
Consequently, effective buffer allocation between the disassembly, repair, and reassembly stages is crucial for absorbing this variability. To optimize buffer allocation in this stochastic environment, this study employed discrete-event simulation to model the detailed MRO process. A multi-objective meta-heuristic algorithm was then applied to identify near-optimal buffer allocations that simultaneously maximize engine inter-arrival rates and minimize work-in-process. The results demonstrate that strategically designed buffers, particularly between major process stages, can significantly enhance performance in the face of uncertainty inherent to MRO operations. This simulation-based optimization approach offers valuable insights for managing complex remanufacturing systems such as aircraft engine MRO.