An Optimization Study of the Time-Index and Arc-Time-Indexed Models and the Iga Metaheuristic for the Aircraft Sequencing Problem

Objective: This study investigates optimization strategies for the Aircraft Sequencing Problem, aiming to increase runway efficiency and reduce fuel costs. To this end, mixed-integer linear programming (MILP) and metaheuristic approaches are analyzed. Theoretical Framework: The study is based on data from the Airports Council International (ACI) on air traffic growth and the research of Yu et al., Mehta et al., and Farhadi et al., which highlight the impact of congestion and the importance of optimizing landing and takeoff sequencing. Method: The research compares MILP models and metaheuristics, incorporating an initial solution to enhance computational efficiency. Seventy-five real-world instances are analyzed, and the solvers CPLEX and GUROBI are compared in terms of optimal solutions and optimality gaps. Results and Discussion: The inclusion of initial solutions improves the efficiency of MILP models. CPLEX and GUROBI show distinct performances: one excels in obtaining optimal solutions, while the other achieves a lower optimality gap. Research Implications: The findings provide insights for airport management, assisting in operational planning, cost reduction, and the improvement of optimization software. Originality/Value: This study contributes by comparing optimization approaches for aircraft sequencing, impacting operational efficiency, cost reduction, and air traffic management improvement.