Integrated airline recovery considering passengers’ decision-making under disruptions

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.