The crew pairing problem (CPP) is central to optimal planning and scheduling of operations in the airline industry, where the objective is to assign crews to cover a flight schedule at minimal cost while adhering to various logistical, personnel, and policy constraints. Despite the implementation of optimized schedules, operations are frequently disrupted by unforeseen events. This vulnerability stems from the deterministic nature of the CPP's base formulation, which fails to account for the uncertainties inherent in real-world operations. Existing solutions either aim to safeguard against a specified level of uncertainty or focus on worst-case scenarios. To this end, we propose a reliability-centric CPP formulation amenable to solution by column-generation (CG) SurvCG, that leverages survival analysis for dynamic quantification of uncertainty using the operation patterns in historical data. Applied to CPP, SurvCG forecasts and incorporates flight connection reliability into the optimization process. Through rigorous experiments on a large-scale first-of-its-kind real-world instance under regular and irregular operating conditions, we demonstrate that SurvCG achieves unprecedented improvements (up to 61%) over baseline in terms of total propagated delays, establishing SurvCG as the first data-driven solution for uncertainty-aware reliable scheduling.
