An efficient hybrid particle swarm optimization algorithm in a rolling horizon framework for the aircraft landing problem

• A hybrid particle swarm optimization algorithm in a rolling horizon framework is proposed to solve the aircraft landing problem.
• The performance of the proposed algorithm is evaluated using benchmark instances involving upto 500 aircrafts and 5 runways.
• The proposed algorithm outperforms the existing approaches in the literature.
• The proposed algorithm is effective in solving the problem in short computational time.

This paper proposes a hybrid particle swarm optimization algorithm in a rolling horizon framework to solve the aircraft landing problem (ALP). ALP is an important optimization problem in air traffic control and is well known as NP-hard. The problem consists of allocating the arriving aircrafts to runways at an airport and assigning a landing time to each aircraft. Each aircraft has an optimum target landing time determined based on its most fuel-efficient airspeed and a deviation from it incurs a penalty which is proportional to the amount of deviation. The landing time of each aircraft is constrained within a specified time window and must satisfy minimum separation time requirement with its preceding aircrafts. The objective is to minimize the total penalty cost due to deviation of landing times of aircrafts from the respective target landing times. The performance of the proposed algorithm is evaluated on a set of benchmark instances involving upto 500 aircrafts and 5 runways. Computational results reveal that the proposed algorithm is effective in solving the problem in short computational time.

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