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Due to the upward trend in the aviation industry, the existing approaches for air traffic control need to be improved to achieve efficient schedules. This paper deals with the aircraft landing problem, which consists of determining a landing time for each aircraft within the radar range of an airport and allocating it to a runway. We propose an exact solution approach that involves mixed-integer linear programming. The objective is hereby to minimize the sum of weighted deviations from the target landing times under consideration of different safety, efficiency and fairness constraints. Despite of the problem’s NP-hardness, our method exhibits low execution times thanks to a modified modeling strategy and provides near-optimal results. Numerical experiments prove efficiency of the approach for different large airports.

A two-stage stochastic programming algorithm based on particle swarm optimization is proposed for an optimal and robust design in the aircraft sequencing and scheduling problem. The first and second stage decision problems are defined as aircraft sequencing and scheduling, respectively, and the uncertainties that arise from the aircraft flight time between two successive fixes are considered. Particle swarm optimization is utilized for a randomized search to make the first stage decision under incomplete information about uncertain parameters. In addition, the random key representation is adopted to apply particle swarm optimization to a discrete aircraft sequencing problem. Then, the second stage decision is made using mixed integer linear programming after the realization of the uncertain parameters. The performance of the proposed algorithm is verified by numerical simulations. [ABSTRACT FROM AUTHOR]

This paper proposes a new sequencing and scheduling algorithm for the point merge system based on mixed integer linear programming considering the uncertain flight time of aircraft. In the first stage of the proposed algorithm, for a static environment, deterministic robust solutions are determined. To consider the uncertainty, an extra buffer is introduced in the sequencing and scheduling algorithm, and the buffer size is analytically derived by generating a deterministic robust counterpart problem. In the second stage, to compensate for unforeseen situations under a dynamic environment, the static solution determined in the first stage is adjusted by using the proposed heuristic algorithm, with a sliding time window to reduce the computational load. The performances of the proposed algorithm are verified via numerical simulations based on historical data analysis. [ABSTRACT FROM AUTHOR]