Modified imperialist competitive algorithm for aircraft landing scheduling problem

In recent years, airport runways have become a more critical bottleneck in airports, and it is very unusual to use only one runway to solve the Aircraft Landing Problem (ALP). The ALP includes the aircraft's landing scheduling and assigning them to runways. In addition to certain limited time frames for aircraft while landing, to prevent accidents, the distance between aircraft should be restricted during the flight and in the landing phase. In this paper, to solve the problem in multi-runway mode, a solution is proposed that has considered all the limitations to create a trade-off between the runways to reduce the traffic in the runways. The present study considers the balance between bands and offers a new method of improving the Imperialist Competitive Algorithm (ICA) while reducing the cost due to the early and late landing of the aircraft. In other words, a novel approach for addressing the ALP with multiple criteria, employing a delay and early landing cost optimization technique and runway balance strategy, as well as using multi-runway, which will reflect the current realities of the aviation industry and provide a more accurate and relevant analysis, has been presented. Thirty-two benchmark instances were selected and compared with four famous algorithms: Particle Swarm Optimization (PSO), Immunoglobulin-Based Artificial Immune System (IAIS), Grey Wolf Optimizer (GWO), and flower pollination algorithm (FPA) as the results on a small-scale indicate, the ICA method performs superior outcomes, except for one case. Regarding the two objectives, the presented method, compared to other methods, managed to reduce the cost by 3.5% (on a small-scale). Furthermore, on a large scale (500 aircraft), improved ICA has been able to reduce the cost of the early or late arrival of the aircraft by 35%.