Single and Multi-objective Aircrafts Landing Scheduling in Dynamic Environment

Air travel has significantly grown as a fast and safe means of transportation. Therefore, creating a smooth air traffic and proper flight scheduling for safe landings with minimal time changes is necessary to avoid wasting time and money. In most studies, the aircraft landing scheduling problem has been considered a static issue. However, this challenge has a dynamic nature in real-world problems. One of the optimizing approaches in this scope is swarm intelligence optimization algorithms, which are simple and highly capable in solving optimization problems. Among these algorithms, Spider-Monkey optimization algorithm is more efficient than traditional algorithms by using few parameters, maintaining search history, controlling searches, and grouping members of the population if needed. In this study, an active scheduling method for aircraft landing scheduling using Spider-Monkey optimization algorithm and genetic algorithm has been presented. The proposed method is examined by some datasets of single and multi-runways (single and multi-objective aircraft landing). The achieved results show an improvement in flight schedules and reduced costs.