Disruption management is critical for airline operations, yet existing recovery models often assume deterministic disruption durations, limiting their effectiveness in real-world, uncertain environments. This paper addresses the integrated airline recovery problem under uncertain disruptions. To capture this uncertainty, delay times are modeled as fuzzy variables and a fuzzy chance-constrained programming model is developed, aimed at minimizing total recovery costs. The model is transformed into a deterministic equivalent using trapezoidal fuzzy numbers. An improved Greedy Randomized Adaptive Search Procedure (GRASP) algorithm is designed to efficiently solve the problem, balancing solution quality and computational efficiency through insert, exchange, and cancel. The local search process is enhanced by incorporating the acceptance criteria of the simulated annealing algorithm. The proposed method is validated using real-world airline data. Results show that, compared to the traditional GRASP algorithm, the improved GRASP algorithm can obtain better solutions in a shorter time; the solutions in deterministic scenarios tends to be more conservative, leading to resource waste; the proposed method can achieve airline recovery at the minimum recovery cost. Sensitivity analysis reveals that selecting an appropriate confidence level significantly influences recovery costs. This paper provides a robust framework for enhancing operational resilience and passenger satisfaction under uncertain conditions, offering practical insights for real-world application.

This paper investigates the integrated airline recovery problem by incorporating passengers’ decision-making, with disrupted passengers categorized into three types: those experiencing delays, those opting for refunds, and those denied boarding due to capacity constraints. To enhance operational flexibility, an improvement is made to the aircraft swap recovery strategy, allowing the replacement of larger aircraft with smaller ones. A bi-objective mathematical model is then constructed to minimize both airline recovery costs and passenger utility loss, and the Nondominated Sorting Genetic Algorithm II (NSGA-II) is designed to solve the model. Computational results demonstrate that the proposed approach outperforms the benchmark plan, achieving average reductions of 17.15% in airline recovery costs and 16.59% in passenger utility loss. Scenario analysis further reveals that the improved aircraft swap strategy is particularly effective for small-scale disruptions, whereas the explicit consideration of passengers’ decision-making yields superior solutions in large-scale disruption scenarios. Overall, this study provides a more realistic recovery framework that balances operational efficiency with passenger-centric considerations, offering valuable insights for airline disruption management.