A comprehensive optimization framework for airport site selection

This study examines airport site selection through various lenses by establishing evaluation objectives and indicator systems. It comprehensively assesses factors such as safety, social impact, stability, economic viability, and airport support capacity, leading to the creation of restrictive, risk, and performance indicators. These indicators inform four objective functions: demand satisfaction, accessibility, risk value, and degree centrality, which collectively form a multi-objective optimization problem. To reduce computational complexity while considering restrictive factors, GIS (Geographic Information System) is employed for preliminary area screening. A hybrid genetic algorithm, combining BRKGA (Biased Random Key Genetic Algorithm) and AGE-MOEA-II (an enhanced front modeling-based MOEA), is then applied to effectively derive Pareto solutions. Lastly, variants of CRITIC (Criteria Importance Through Intercriteria Correlation) and TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) are used to select the final solution from the Pareto set. The paper also investigates variations in objective functions and their interrelations with different numbers of selected airports, offering valuable insights for future airport site selection.