Enhancing Aircraft Landing Efficiency Through Greedy Hash Map Scheduling

Aircraft route planning and landing pad allocation urgently need to be optimized due to the rise in air traffic worldwide and the intricate and ever-changing nature of both military and commercial aviation operations. These jobs are made more difficult by airspace limits, weather unpredictability, and the availability of runways and landing pads. A decision support system (DSS) that incorporates multicriteria optimization to address the aircraft landing and route scheduling problem is presented in this study. The system’s objectives are to minimize fuel consumption, optimize flight paths, and dynamically assign landing pads while maintaining adherence to crucial operating limitations like weather, runway capacity, no-fly zones, and aircraft fuel levels. The system can process and manage feedback data in real time with hash tables and a greedy algorithm for using weather information, aircraft specifications, and air traffic control. Simulation results show the technology can improve fuel consumption, boost the operational efficiency and minimize delays to possibly benefit military and commercial aviation. As the system is made scalable and more sophisticated by employing machine learning in future versions, they will further improve upon scalability and decision making.