This paper addresses the problem of optimizing the departure sequence of aircraft at busy airports, taking into account the prescribed departure trajectories (SIDs or omnidirectional trajectories) and the aircraft categories. The goal is to improve the efficiency of air traffic management by reducing delays and optimizing the use of departure trajectories. The proposed algorithm is based on the Shortest Job First (SJF) concept, which sequences aircraft based on the estimated time to reach the holding point, thereby improving the utilization of departure trajectories. The study compares the performance of different numbers of aircraft and shows the time savings achieved compared to the First Come First Served (FCFS) method. Air transport is continuously expanding, but airport infrastructure is not keeping up, leading to increased pressure on air traffic controllers. The main cause of delays is the imbalance between demand and air capacity, which is exacerbated by factors such as weather and technical issues. Delays are more pronounced at airports than during the en-route phase, highlighting the inefficiency of taxiing and waiting at holding points. The paper focuses on the aircraft sequencing problem (ASP) after takeoff, considering the impact of aircraft types, their performance, and the sequence on the ground. The use of SIDs is influenced by the speed of aircraft, as slower aircraft take longer to clear the SID. The sequence of aircraft on the ground also affects the use of SIDs, as low-performance aircraft preceding high-performance ones can lead to longer delays. The state of the art includes various studies that propose methods to optimize aircraft sequencing, such as using reference business trajectories to reduce controller interventions, data-splitting algorithms for solving the ASP as a mixed-integer program, and real-time models to reduce waiting times at runway holding points. Other studies focus on real-time optimization of takeoff and landing operations, dynamic models, and genetic algorithms for solving the ASP. The paper also discusses the application of the ASP as a parallel machine scheduling problem with unequal ready times, target times, and deadlines, using greedy heuristics and metaheuristics to find efficient solutions. The study highlights the importance of separation minima and operational constraints in optimizing aircraft sequencing.This paper addresses the problem of optimizing the departure sequence of aircraft at busy airports, taking into account the prescribed departure trajectories (SIDs or omnidirectional trajectories) and the aircraft categories. The goal is to improve the efficiency of air traffic management by reducing delays and optimizing the use of departure trajectories. The proposed algorithm is based on the Shortest Job First (SJF) concept, which sequences aircraft based on the estimated time to reach the holding point, thereby improving the utilization of departure trajectories. The study compares the performance of different numbers of aircraft and shows the time savings achieved compared to the First Come First Served (FCFS) method. Air transport is continuously expanding, but airport infrastructure is not keeping up, leading to increased pressure on air traffic controllers. The main cause of delays is the imbalance between demand and air capacity, which is exacerbated by factors such as weather and technical issues. Delays are more pronounced at airports than during the en-route phase, highlighting the inefficiency of taxiing and waiting at holding points. The paper focuses on the aircraft sequencing problem (ASP) after takeoff, considering the impact of aircraft types, their performance, and the sequence on the ground. The use of SIDs is influenced by the speed of aircraft, as slower aircraft take longer to clear the SID. The sequence of aircraft on the ground also affects the use of SIDs, as low-performance aircraft preceding high-performance ones can lead to longer delays. The state of the art includes various studies that propose methods to optimize aircraft sequencing, such as using reference business trajectories to reduce controller interventions, data-splitting algorithms for solving the ASP as a mixed-integer program, and real-time models to reduce waiting times at runway holding points. Other studies focus on real-time optimization of takeoff and landing operations, dynamic models, and genetic algorithms for solving the ASP. The paper also discusses the application of the ASP as a parallel machine scheduling problem with unequal ready times, target times, and deadlines, using greedy heuristics and metaheuristics to find efficient solutions. The study highlights the importance of separation minima and operational constraints in optimizing aircraft sequencing.