A genetic local search algorithm with a threshold accepting mechanism for solving the runway dependent aircraft landing problem

As the demand for air transportation continues to grow, some flights cannot land at their preferred landing times because the airport is near its runway capacity. Extra fuel consumption and air pollution are then caused by the landing delays. Moreover, such delays may possibly yield extra costs for both passengers and airline companies that result from rescheduling transfer passengers and crew members. Consequently, how to increase the handling efficiency of congested airports is a crucial management issue. Building new runways at existing airports is often not feasible due to environmental, financial and geographical constraints. Therefore, devising a method for tackling the aircraft landing problem (ALP) in order to optimize the usage of existing runways at airports is the focus of this paper. This paper aims to develop a solution procedure based on a genetic local search (GLS) algorithm for solving the ALP with runway dependent attributes. A set of numerical experiments were conducted to test the validity of the proposed algorithm based on five test instances created and investigated by previous studies. The numerical results showed that the proposed GLS algorithm can effectively and efficiently determine the runway allocation, sequence and landing time for arriving aircraft for the five test cases by minimizing total delays under the separation constraints in comparison with the outcomes yielded by previous studies.