Artificial Neural Network Modeling for Airline Disruption Management

TL;DR

This paper develops a modular artificial neural network system to improve airline disruption management by accurately predicting recovery impacts and accommodating new operational capabilities like unmanned aerial systems.

Contribution

It introduces a neural network-based predictive transfer function model and a modular ensemble approach for airline disruption management, enhancing flexibility and accuracy.

Findings

Accurately estimates recovery impacts during flight disruptions.

Ensures compliance with industry tardiness standards.

Provides a scalable, modular framework for ADM.

Abstract

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.