Airport Scheduling: A Summary of Three Scenarios on the Application of Machine Learning and Genetic Algorithms

Abstract

Airport scheduling has an important optimization value in air transportation, and its complexity and multiple constraints make it difficult for existing methods to deal with it comprehensively. This paper is a review paper, that systematically summarizes the application of genetic algorithms and machine learning in the scheduling of flights, the scheduling of airport resource allocation, and the scheduling of airport external resource allocation. Through the analysis of many studies, this paper summarizes the advantages and disadvantages of different algorithms in various scheduling scenarios and points out the advantages of genetic algorithms in dealing with complex multi-objective optimization problems. In addition, this paper explores the potential of combining machine learning with genetic algorithms to further enhance the effect of scheduling optimization. This paper provides an important reference for future research and helps the innovation and practical application of methods in the field of airport scheduling.