OPTIMIZATION OF AIRCRAFT SEQUENCING USING MIXED-INTEGER PROGRAMMING

The rapid growth in global air travel has intensified congestion and delays, highlighting the need for improved aircraft sequencing strategies in air traffic management. In this study, we present a Mixed-Integer Programming (MIP) model to address the Aircraft Landing Problem (ALP), incorporating key operational constraints, including threshold changes. The model is adapted to the context of Guarulhos International Airport using real traffic data from selected days in 2023. Its objective is to minimize cumulative deviations from unimpeded landing times, thereby improving runway use efficiency. Performance is evaluated by comparing model-generated sequences against the First-Come, First-Served (FCFS) baseline across various scenarios, including high-traffic periods and threshold changes. The results demonstrate significant improvements over FCFS, with an average reduction of 39.12% in cumulative deviations on days without threshold changes, and 20.58% under threshold change conditions. These findings confirm the model's potential to enhance sequencing efficiency and support air traffic management.