Robust long-term aircraft heavy maintenance check scheduling optimization under uncertainty

Long-term heavy maintenance check schedules are crucial in the aviation industry since airlines need them to prepare required tools, workforce, and aircraft spare parts. However, most adopt a manual approach plan current practice. This process relies on experience of their planners, resulting frequent adjustment because uncertainty. paper applies genetic algorithm (GA) generate robust schedules. It aims reduce workload frequency revising considering uncertainties associated with duration daily utilization. A major European airline case study shows that GA finds efficient multi-year for fleet 45 30 min. Compared followed by airline, reduces total number checks 7% while increasing utilization 4.4%, which could potentially lead reduction direct annual costs between $122.5K $612.5K. Furthermore, when testing robustness 4-years produced, Monte Carlo analysis has shown all be maintained before due date 41% episodes simulated, compared 0.27% single deterministic scenario approach.