The idea in this research is to propose a distributed multi-agent system (distributed either in geographical term of the resources or in relation with finding solutions in a distributed way) that can find the best solution after a disruption has occurred. The solution will have to take into consideration several dimensions of the problem (in the case of the airline operations, flights/aircrafts, crew and passengers) and find the best-integrated solution. An important characteristic will be the pro-active approach of the system in the sense that it will try to avoid similar problems in the future by learning from previous solutions and suggesting changes to be included in future schedules/plans. This research should be focused in the disruption management problem in a general context. The Airline Operations (AOC) will provide the environment to test our approach (taken from a real airline company information system). If information is available, other application domains like Air Traffic Control (ATC) and Autonomous Spacecraft Control (ASC) will be used to test our approach. All these application domains will offer valuable information to be used as case studies during the thesis and in papers submitted to conferences/journals.

“Disruption Management in Airline Operations Control - An Intelligent Agent-Based Approach”, Antonio J.M. Castro and Eugenio Oliveira,
Web Intelligence and Intelligent Agents, edited by Zeeshan Usmani, ISBN 978-953-7619-85-4, IN-TECH 2010.

"Minimizing Airport Peaks Problem by Improving Airline Operations Performance through an Agent Based System", Antonio J.M. Castro, Antonio Mota, Luis Paulo Reis and Eugenio Oliveira, New Trends in Artificial Intelligence, Local Proceedings of the 14th Portuguese Conference on Artificial Intelligence (EPIA2009), pp. 3-14, ISBN: 978-972-96895-4-3, Universidade de Aveiro, Portugal, October 12-15, 2009.

"Recovering from Airline Operational Problems with a Multi-Agent System: a Case Study", Antonio Mota, Antonio J. M. Castro and Luis Paulo Reis,  Lecture Notes in Artificial Intelligence (LNAI 5816), Springer, Progress in Artificial Intelligence, 14th Portuguese Conference on Artificial Intelligence (EPIA2009), pp. 461-472, ISBN: 978-3-642-04685-8, Universidade de Aveiro, Portugal, October 12-15, 2009.

“Quantifying Quality Operational Costs in a Multi-Agent System for Airline Operations Recovery”, Antonio J.M. Castro and Eugenio Oliveira, International Review on Computers and Software (I.RE.CO.S), Vol. 4, No. 4, pp. 504-516, ISSN 1828-6003, July 2009.

“Using Quality Costs in a Multi-Agent System for an Airline Operations Control”, Antonio J.M. Castro and Eugenio Oliveira, 11th International Conference on Enterprise Information Systems (ICEIS 2009), Artificial Intelligence and Decision Support Systems, pp. 19-24, Milan, Italy, May 6-10, 2009. ISBN: 978-989-8111-85-2

“A Multi-Agent System for Airline Operations Control”, Antonio J.M. Castro and Eugenio Oliveira, 7th International Conference on PAAMS’09, Advances in Intelligent and Soft Computing 55, pp. 159-168, Springer-Verlag Berlin Heidelberg 2009. ISBN: 978-3-642-00486-5

“Solving Airline Operations Problems using Specialized Agents in a Distributed Multi-Agent System”, Antonio J.M. Castro and Eugenio Oliveira
LNBIP 12 (ICEIS 2007), pp. 173-184, Springer-Verlag Berlin Heidelberg 2008, ISBN: 978-3-540-88709-6

“The Rationale behind Development of an Airline Operations Control Centre using GAIA based Methodology", Antonio Castro and Eugenio Oliveira
Int. J. Agent-Oriented Software Engineering, Vol. 2, No. 3, pp.350-377, 2008

“A Multi-Agent System for Airline Operations Control”
Presented at PAAMS 2009 in March, 26th 2009. Salamanca, Spain.

“MASDIMA: A Multi-Agent System for an Airline Operations Control Center”
Presented at NASA AMES Research Center in January 2009. Moffett Field, USA.

“Using Specialized Agents in a Distributed MAS to Solve Airline Operations Problems: a Case Study”
Presented at IAT 2007 in December 2007. Silicon Valley, USA.