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