Air-rail intermodal collaborative decision making for aircraft recovery from airport station disruption

Abstract

During airport disruptions caused by capacity shortages, it is crucial for airlines to have an effective recovery plan to minimize losses and prevent the spread of disruptions and delays. This study proposes an air-rail intermodal Collaborative Decision Making (CDM) approach, which recommends incorporating High-Speed Railway (HSR) transportation into the management of aircraft recovery from airport station disruptions. The structural properties of the proposed model indicate employing a Lagrangian relaxation with subgradient methods to effectively obtain near-optimal solutions. A framework for developing Lagrangian heuristics (heuristics based on Lagrangian relaxation and sub-gradient optimization) is proposed to obtain solutions. Additionally, the study proposes a modified aircraft recovery model considering the downstream effects of flight delays and cancellations during the airport disruption recovery period and introduces slack variables to linearize the model. The computational experiments conducted in this study demonstrate the effectiveness of the proposed air-rail intermodal strategy for managing airport disruptions. Experiments conducted on large-scale datasets demonstrate that the Lagrangian Relaxation method outperforms both the Benders method and the Genetic Algorithm in terms of both computational speed and solution quality.

This research provides valuable insights into the management of airport disruptions and offers practical solutions for airlines to mitigate the impact of capacity shortages.