ASSESSING AIRCRAFT LANDING SCHEDULES WITH ARTIFICIAL INTELLIGENCE
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As aircraft takes off and lands in this era, airport scheduling operations are crucial. The radar range control system controls aircraft scheduling. Arrival runways are critical to air traffic control. A delay in arrival affects an airline's operations and costs. Within 200 nautical miles (370 kilometers), radar sends the aircraft's position. This research provides details about aircraft execution times and penalties. These formulations can model numerous practical issues (aircraft selection, priority restrictions, limiting landing and takeoff numbers, runway workload balancing). Since existin...
As aircraft takes off and lands in this era, airport scheduling operations are crucial. The radar range control system controls aircraft scheduling. Arrival runways are critical to air traffic control. A delay in arrival affects an airline's operations and costs. Within 200 nautical miles (370 kilometers), radar sends the aircraft's position. This research provides details about aircraft execution times and penalties. These formulations can model numerous practical issues (aircraft selection, priority restrictions, limiting landing and takeoff numbers, runway workload balancing). Since existing techniques do not consider time, penalties are very expensive based on time. Whenever possible, penalty costs are reduced during flights, landings, and takeoffs. Flights land on that runway at that time without congestion. This is a problem if it occurs. Particle Swarm Optimization Technique, artificial neural networks, and genetic algorithms are used. As a result, the runway is not congested. Moreover, this technique reduces the amount of penalties to be charged.
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