Several studies have demonstrated that an integrated approach to the airline schedule recovery problem, optimising multiple facets simultaneously rather than using traditional sequential methods, yields improved solution quality; however, at the cost of model simplification, with most studies focusing solely on cost minimisation. This paper introduces a multi-objective Benders decomposition approach to a detailed integrated aircraft and crew recovery model that considers a heterogeneous fleet, individual crew members, and basic passenger considerations, thereby addressing both model simplifications and lack of multi-objective considerations. A lexicographic multi-objective optimisation scheme is integrated into Benders decomposition, allowing for the optimisation of multiple distinct objectives: minimising cancellations, minimising recovery costs, maximising on-time performance, and minimising changes made. Computational tests were conducted using real schedule data provided by Transavia, with results showing high-quality solutions within the model’s defined assumptions. However, Benders decomposition alone was insufficient to be applicable within live disruption management, with performance times ranging from 5 to 30 minutes depending on the scenario’s complexity. The model’s design and ability to consider crew on an individual level show strong promise in solution quality and applicability to the industry, as crew are often considered at both the pairing and individual levels. This provides a strong foundation for practical disruption management support tools if deficiencies in time performance can be addressed.

Disruptive events pose a significant challenge to airlines’ everyday operations due to the highly optimized nature of their schedules. Unforeseen events force airlines to rapidly reschedule and adjust their operations. Current disruption management methods rely mostly on reactive and static models that fail to capture the dynamic and probabilistic nature of airline recovery. This study presents a model-based reinforcement Reinforcement Learning (RL) method for aircraft recovery under disruption uncertainty that anticipates future potential disruptions. The Aircraft Recovery Problem (ARP) is formulated as a Markov Decision Process (MDP) and a framework is proposed in which an Approximate Dynamic Programming (ADP) algorithm that relies on Value Function Approximation (VFA) determines optimal recovery actions considering the immediate and future impact of each action. The uncertain disruptions are modelled as aircraft unavailabilities with a fixed probability of realizing. The aim of the model is to keep flight delays and cancellations at a minimum while exploiting stochastic information on potential aircraft unavailabilities. The model is tested on multiple scenarios with different objectives and levels of disruptions and is benchmarked against an exact optimization algorithm. Results indicate that a proactive approach outperforms reactive models, particularly in high-disruption scenarios with high aircraft utilization. The comparison with the exact benchmark shows that the RL method can achieve sub-optimal solutions with considerably less corrective actions. This framework offers a decision support tool that allows airline operators to find more resilient solutions in uncertain environments by incorporating probabilistic predictions on disruptions in the decision-making process

Model predictive control is an optimal, model-based control method that has the powerful capability of directly including input and output constraints. Next to this, it is known that helicopters are hard to fly with its complex, unstable and highly coupled dynamics. With the introduction of the concept of handling qualities, guidelines for helicopter and flight control system design were set in the ADS-33 document to improve the ease of controlling rotorcraft. In order to improve helicopter handling qualities, this paper investigates whether linear and nonlinear MPC are suitable for online application to helicopters to reduce cross-coupling effects. This was investigated by evaluating its performance on the cross-coupling requirements of the ADS-33 handling quality document. It was found that both linear and nonlinear MPC are very effective to reduce cross-coupling effects even when disturbances or prediction model errors are present. The model predictive controller could reduce the off-axis coupling response by around 99% compared to the uncontrolled helicopter. Furthermore, it performed 90% to 99% better than a PID controller in most coupling cases.