Optimization of airport ground operations integrating genetic and dynamic flow management algorithms
This paper(1) presents a new method for automatically finding 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 as fast as possible: a genetic algorithm and a time-space dynamic flow management algorithm. An integrated system to combine the strengths of each algorithm 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 the search space grows. The performance of this combination is illustrated using simulated samples of a real-world scenario: ground operations at Madrid Barajas International Airport.
airport ground operations routing scheduling network flow management hybrid genetic algorithms