Since the 1970s, most airlines have incorporated computerized support for managing disruptions during flight schedule execution. However, existing platforms for airline disruption management (ADM) employ monolithic system design methods that rely on the creation of specific rules and requirements through explicit optimization routines, before a system that meets the specifications is designed. Thus, current platforms for ADM are unable to readily accommodate additional system complexities resulting from the introduction of new capabilities, such as the introduction of unmanned aerial systems, operations, and infrastructure, to the system. To this end, historical data on airline scheduling and operations recovery are used to develop a system of artificial neural networks (ANNs), which describe a predictive transfer function model (PTFM) for promptly estimating the recovery impact of disruption resolutions at separate phases of flight schedule execution during ADM. Furthermore, this paper provides a modular approach for assessing and executing the PTFM by employing a parallel ensemble method to develop generative routines that amalgamate the system of ANNs. Our modular approach ensures that current industry standards for tardiness in flight schedule execution during ADM are satisfied, while accurately estimating appropriate time-based performance metrics for the separate phases of flight schedule execution.
Artificial Neural Network Modeling for Airline Disruption Management
Journal of Aerospace Information Systems ; 19 , 5 ; 382-393
2022-02-11
12 pages
Article (Journal)
Electronic Resource
English
Airline disruption management-Perspectives, experiences and outlook
Online Contents | 2007
|Airline disruption management—Perspectives, experiences and outlook
Elsevier | 2007
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