  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 (UAS), operations and infrastructure, to the system. To this end, we
use historical data on airline scheduling and operations recovery 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, we provide 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.
