Authors:
Henrique Sousa
1
;
Ricardo Teixeira
1
;
Henrique Lopes Cardoso
2
and
Eugénio Oliveira
2
Affiliations:
1
Universidade do Porto, Portugal
;
2
Universidade do Porto, LIACC and Laboratório de Inteligência Artificial e Ciência de Computadores, Portugal
Keyword(s):
Aircraft Scheduling, Disruption Management, Ant Colony Optimization.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Constraint Satisfaction
;
Evolutionary Computing
;
Formal Methods
;
Industrial Applications of AI
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Planning and Scheduling
;
Simulation and Modeling
;
Soft Computing
;
Symbolic Systems
Abstract:
Disruption management is one of the main concerns of any airline company, as it can influence its annual
revenue by upwards of 3%. Most of medium to large airlines have specialized teams which focus on
recovering disrupted schedules with very little automation. This paper presents a new automated approach
to solve both the Aircraft Assignment Problem (AAP) and the Aircraft Recovering Problem (ARP), where
the solutions are responsive to unforeseen events. The developed algorithm, based on Ant Colony
Optimization, aims to minimize the operational costs involved and is designed to schedule and reschedule
flights dynamically by using a sliding window. Test results tend to indicate that this approach is feasible,
both in terms of time and quality of the proposed solutions.