A Model for the airport gate assignment problem (AGAP) with greenhouse gas emissions considerations

• Developed an innovative algorithm for optimizing airport gate flight assignments. • Genetic algorithms and Nash equilibrium reduce unassigned flights and taxiing distances. • Achieved a 1356 kg CO2 reduction in a 4-hour simulation at a scaled-down high-demand airport. • Enhanced operational efficiency, passenger comfort, and environmental sustainability. • Supports flight reassignment to address last-minute delays and airport congestion. • Combines multi-criteria optimization for sustainable and efficient airport operations. This paper investigates the problem of optimally assigning airplanes to airport gates (Airport Gate Assignment Problem—AGAP), with GHG emissions considerations. A multi-objective mathematical programming model is formulated with the objectives of minimizing flights assigned to apron gates, walking distances of connecting passengers, and GHG emissions because of airplane taxiing movements. A genetic algorithm, which incorporates a Nash equilibrium control element, is used to solve the problem. The Nash equilibrium element allows the fast and efficient identification of those genetic algorithm parameters that yield the best solution to the problem. The model and solution process are demonstrated in a hypothetical airport with ten gates, using simulated flight schedule data, which resemble operations of a real-world scaled-down airport with high passenger traffic. Results indicate considerable GHG emission savings and improved passenger walking burden at the airport over a simplified assignment approach. Specifically, the algorithm achieved a reduction of 1356.6 Kg CO2 emissions compared to non-optimized flight distribution, i.e. an average reduction of about 19 liters per aircraft, as well as an additional indirect reduction in total CO2 emissions due to a reduction in delays and congestion.