Artificial Neural Network Modeling for Airline Disruption Management

Since the 1970s, most airlines have incorporated computerized support for managing disruptions during flight schedule execution. However, existing platforms airline disruption management (ADM) employ monolithic system design methods that rely on creation of specific rules and requirements through explicit optimization routines, before a meets specifications is designed. Thus, current ADM are unable to readily accommodate additional complexities resulting from introduction new capabilities, such as unmanned aerial systems, operations, infrastructure, system. To this end, historical data scheduling operations recovery used develop artificial neural networks (ANNs), which describe predictive transfer function model (PTFM) promptly estimating impact resolutions at separate phases execution ADM. Furthermore, paper provides modular approach assessing executing PTFM by employing parallel ensemble method generative routines amalgamate ANNs. Our ensures industry standards tardiness in satisfied, while accurately appropriate time-based performance metrics