Aircraft routing networks are complex systems vulnerable to cascading delays triggered by weather disruptions and airspace constraints. This paper proposes a Distributionally Robust Aircraft Routing (DRAR) model for systemic risk assessment. Conventional robust or stochastic optimization methods often rely on specific assumptions about delay distributions (e.g., fixed probability distributions or scenario sets). However, due to the suddenness and multi-source nature of flight delays, their true distribution is difficult to accurately characterize, limiting the effectiveness of these methods in real-world uncertain conditions. By constructing a Wasserstein-metric ambiguity set, the proposed model captures distributional uncertainty without assuming fixed probabilities, thereby handling delay risks more robustly. The study incorporated chance constraints to bound extreme delay probabilities and reformulated the model as a tractable mixed-integer program. Experiments on real airline data demonstrate that DRAR outperforms traditional benchmarks, reducing propagation delays by 4–6%, volatility by 7–9%, and extreme delay risks by up to 15.7%. Thus, the model provides a practical tool for aviation decision-makers: airlines can leverage it to optimize aircraft scheduling and routing, systematically mitigate delay propagation risk, control the probability of extreme delays, and consequently reduce indirect operational costs arising from crew overtime and airport scheduling conflicts, thereby enhancing overall resource efficiency and operational resilience. These results validate DRAR as an effective tool for controlling tail risks and ensuring sustainable operations in uncertain aviation environments.
Wen et al. (Mon,) studied this question.