Title: New air traffic management approach based on expert system and using real-time scheduling algorithms

Authors: Sallami Chougdali; Asmaa Roudane; Khalifa Mansouri; Mohamed Youssfi; Mohammed Qbadou

Addresses: Signals, Distributed Systems and Artificial Intelligence Laboratory, ENSET, Hassan II University, Casablanca, Morocco ' Signals, Distributed Systems and Artificial Intelligence Laboratory, ENSET, Hassan II University, Casablanca, Morocco ' Signals, Distributed Systems and Artificial Intelligence Laboratory, ENSET, Hassan II University, Casablanca, Morocco ' Signals, Distributed Systems and Artificial Intelligence Laboratory, ENSET, Hassan II University, Casablanca, Morocco ' Signals, Distributed Systems and Artificial Intelligence Laboratory, ENSET, Hassan II University, Casablanca, Morocco

Abstract: The air traffic management operations are considered as complex problems. In practice, they can formulate as a constrained optimisation problem that needs to be solved in a real-time environment, also they are non-regular problems, so we can use the expert system to schedule the air traffic management operations. Expert systems using extracted cognitive data, inspiring from the human expertise and its best practices. They are based on the machine performance and its ability to carry out a very large number of complex iterations. Almost all air traffic management operations are classified as scheduling operations. The goal of this paper is to present a new intelligent system with an evolving knowledge base and a creative inference engine. This system allows choosing the optimal way based on real time algorithms to schedule all air traffic management operations such as aircrafts' landing scheduling.

Keywords: air traffic management; ATM; intelligent systems; aircraft landing scheduling; expert systems; real-time scheduling.

DOI: 10.1504/IJIEI.2016.080526

International Journal of Intelligent Engineering Informatics, 2016 Vol.4 No.3/4, pp.305 - 321

Received: 01 Oct 2015
Accepted: 07 May 2016
Published online: 28 Nov 2016 *

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