Multiairport capacity management: genetic algorithm with receding horizon
Xiao-Bing Hu
Wen-Hua Chen
Ezequiel A. Di Paolo
2134/4006
https://repository.lboro.ac.uk/articles/journal_contribution/Multiairport_capacity_management_genetic_algorithm_with_receding_horizon/9224498
The inability of airport capacity to meet the growing
air traffic demand is a major cause of congestion and costly delays.
Airport capacity management (ACM) in a dynamic environment
is crucial for the optimal operation of an airport. This paper
reports on a novel method to attack this dynamic problem by
integrating the concept of receding horizon control (RHC) into a
genetic algorithm (GA). A mathematical model is set up for the
dynamic ACM problem in a multiairport system where flights can
be redirected between airports. A GA is then designed from an
RHC point of view. Special attention is paid on how to choose those
parameters related to the receding horizon and terminal penalty.
A simulation study shows that the new RHC-based GA proposed
in this paper is effective and efficient to solve the ACM problem in
a dynamic multiairport environment.
2008-11-17 16:15:08
Air traffic control
Airport capacity management (ACM)
Genetic algorithm (GA)
Receding horizon control (RHC)
Terminal penalty
Artificial Intelligence and Image Processing
Engineering not elsewhere classified