Buffering Airline Crew Schedules for Flight Duty Periods to Balance Planned Costsand Crew Legality Violations: A Machine Learning Approach

Legality violations pose significant operational challenges for airlines. Introducing buffer times in crew flight duty periods during the planning phase can mitigate these violations but often lead to higher planning costs. This paper presents a machine learning-based framework coupled with simulation-based analysis to balance this trade-off. We developed CatBoost models that accurately predict flight delays, translating these predictions into optimal buffer-time allocations within crew schedules. Our findings highlight the critical role of delay prediction profiles and conservatism in achieving this balance. A case study using American Airlines data demonstrates how our approach helps decision makers identify buffer levels that minimize legality violations while controlling costs.