Optimization of airport ground operations integrating genetic and dynamic flow management algorithms

This work * presents a new method to automatically search the best routes and schedules for airport ground operations, within a decision support system for tower controllers, a hard real-world application. It explores the potential advantages of hybridizing two complementary types of algorithmic approaches to find solutions with minimum delay: a genetic algorithm and a time-space dynamic flow management algorithm. An integration scheme to combine the strengths of each one and exploit their complementary nature has been analyzed. The proposed flow-management algorithm deterministically optimizes an over-simplified problem, while the genetic algorithm is able to search within a more realistic representation of the real problem, but success is not always guaranteed if search space grows. The performance of this combination is illustrated with simulated samples of a real-world scenario: ground operations in the Madrid-Barajas International Airport.