Conference Agenda

Due to the escalating congestion at airports, there has been a growing interest in using optimization methods for airport slot allocation and flight scheduling. Level 2 and 3 airports nowadays have a substantial surplus of demand relative to airport capacity, which requires sophisticated slot allocation. This process is not only a computationally complex task but needs to satisfy the requirements of different players following individual objectives. The resulting allocation of slots and subsequent flight schedules significantly impacts the market power of airlines. From the passengers' perspective, the key consideration is connectivity, emphasizing the importance of viable connections between destinations. We propose an innovative slot allocation model that prioritizes destination coverage while considering limitations on market power for participating airlines. Based on an extensive computational study, we elaborate on how varying degrees of market power restrictions can positively or negatively influence connectivity.

Airlines solve many mathematical optimization problems and combine the resulting solutions to ensure smooth, minimum-cost operations, e.g., the Tail Assignment Problem to determine an optimal assignment of aircraft to a flight schedule.

For these to be effective, the available data and forecasts must reflect the situation as accurately as possible.

However, the underlying plan is subject to severe uncertainties: Staff and demand uncertainties can even lead to flight cancellations or result in entire aircraft having to be grounded.

Therefore, it is advantageous for airlines to protect their mathematical models against uncertainties in the input parameters.

We present two computationally tractable and cost-efficient robust models and solution approaches:

First, we set up a novel mixed integer model for the integrated fleet and tail assignment, which ensures that as few subsequent flights as possible have to be canceled in the event of initial flight cancellations.

We then solve this model using a procedure that ensures that the costs of the solution remain considerably low.

Our second model is an extended fleet assignment model that allows us to compensate for entire aircraft cancellations in the best possible way, taking into account rescheduling options.

We demonstrate the effectiveness of both approaches by conducting an extensive computational study based on real flight schedules of a major German airline.

Both models deliver stable, cost-efficient solutions, which significantly reduce follow-up costs in the case uncertainties arise.

Airlines are responsible for managing the hour-to-cycle performance of their aircraft on operational lease. For a leased aircraft, the leasing contract usually specifies target hour-to-cycle ratios that must be met. Supplemental rental payments are required if the aircraft cannot achieve the target ratio. This study presents a tail assignment problem that takes into account the accumulated flight hours and flight cycles of the aircraft and incorporates penalty costs for failing to meet the target hour-to-cycle ratios. Considering hour-to-cycle ratios in the tail assignment problem adds complexity to the aircraft scheduling process. It also results in nonlinear expressions to the mathematical model. This study presents two possible reformulations of the problem, one using McCormick linearization and the other using second-order conic inequalities. Computational results indicate that mathematical formulation using McCormick linearization performs better than conic reformulation. Furthermore, numerical results demonstrate that disregarding hour-to-cycle ratio targets in aircraft scheduling can lead to drastic deviations from target ratios.