A Novel Data-Driven Design Paradigm for Airline Disruption Management

Airline disruption management traditionally seeks to address three problem dimensions: aircraft scheduling, crew scheduling, and passenger scheduling, in that order. However, current efforts have, at most, only addressed the first two problem dimensions concurrently and do not account for the propagative effects that uncertain scheduling outcomes in one dimension can have on another dimension. Uncertainties in scheduling outcomes originate from random disruption events (like inclement weather and aircraft malfunction), the order in which the events occur, and how they are resolved. As such, these uncertainties propagate through all problem dimensions for airline disruption management on the day of operation.

In addition, existing approaches for airline disruption management include human specialists who decide on necessary corrective actions for airline schedule disruptions on the day of operation. However, human specialists are limited in their ability to process copious amounts of information imperative for making robust decisions that simultaneously address all problem dimensions during disruption management. Therefore, there is a need to augment the decision-making capabilities of a human specialist with quantitative and qualitative tools that can rationalize complex interactions amongst all dimensions in airline disruption management, and provide objective insights to the specialists in the Airline Operations Control Center (AOCC). To that effect, this dissertation provides a discussion and demonstration of an agnostic and systematic paradigm for enabling simultaneously-integrated recovery of all problem dimensions during airline disruption management, through an intelligent multi-agent system that employs principles from artificial intelligence and distributed ledger technology.