Real Time Genetic Scheduling of Aircraft Landing Times

Evolutionary approaches are not usually considered for real time scheduling problems due to long computation times and uncertainty about the length of the computation time. We argue that for some kinds of problems, such as optimizing aircraft landing times, genetic algorithms have advantages over other methods as a best solution is always available when needed, and, since the computation is inherently parallel, more processors can be added to get higher quality solutions if necessary. Furthermore, the computation time can be decreased and the quality of the generated schedules increased by seeding the genetic algorithm from a previous population. We have performed a series of experiments on landing data for Sydney airport on the busiest day of the year. Our results show that high quality solutions can be computed in the time window between aircraft landings.