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 formula...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.展开更多
This paper reviews existing approaches to the airport gate assignment problem （AGAP） and presents an optimization model for the problem considering operational safety constraints. The main objective is to minimize t...This paper reviews existing approaches to the airport gate assignment problem （AGAP） and presents an optimization model for the problem considering operational safety constraints. The main objective is to minimize the dispersion of gate idle time periods （to get robust optimization） while ensuring appropriate matching between the size of each aircraft and its assigned gate type and avoiding the potential hazard caused by gate apron operational conflict. Genetic algorithm is adopted to solve the problem, An illustrative example is given to show the effectiveness and efficiency of the algorithm. The algorithm performance is further demonstrated using data of a terminal from Beijing Capital International Airport （PEK）.展开更多
文摘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.
文摘This paper reviews existing approaches to the airport gate assignment problem （AGAP） and presents an optimization model for the problem considering operational safety constraints. The main objective is to minimize the dispersion of gate idle time periods （to get robust optimization） while ensuring appropriate matching between the size of each aircraft and its assigned gate type and avoiding the potential hazard caused by gate apron operational conflict. Genetic algorithm is adopted to solve the problem, An illustrative example is given to show the effectiveness and efficiency of the algorithm. The algorithm performance is further demonstrated using data of a terminal from Beijing Capital International Airport （PEK）.
