DISTRIBUTIONALLY ROBUST OPTIMIZATION MODEL FOR AIRCRAFT ROUTING PROBLEM CONSIDERING PROLONGED DELAYS

The paper proposes a distributionally robust optimization model based on the Wasserstein metric to solve the aircraft routing problem. The model comprehensively considers the impact of both total propagated delays and prolonged delays on the operational costs of airlines. Given the suddenness and multifaceted nature of flight delays, the study construct an ambiguity set based on the Wasserstein metric with the empirical distribution formed by historical delay data to characterize flight delays. Then a data-driven distributionally robust (DR) chance constraint was proposed to limit the number of prolonged delays on routes. The optimization model is constructed using historical flight schedule data and delay data, and the model's performance is validated with simulated delay data. In the computational experiments, the proposed model demonstrated superior performance compared to the comparison model, indicating that the proposed model is more resilient to the impact of delays on routes, which is a crucial aspect in the operational management of airlines.