  Currently, flight delays are common and they propagate from an originating
flight to connecting flights, leading to large disruptions in the overall
schedule. These disruptions cause massive economic losses, affect airlines'
reputations, waste passengers' time and money, and directly impact the
environment. This study adopts a network science approach for solving the delay
propagation problem by modeling and analyzing the flight schedules and
historical operational data of an airline. We aim to determine the most
disruptive airports, flights, flight-connections, and connection types in an
airline network. Disruptive elements are influential or critical entities in an
airline network. They are the elements that can either cause (airline
schedules) or have caused (historical data) the largest disturbances in the
network. An airline can improve its operations by avoiding delays caused by the
most disruptive elements. The proposed network science approach for disruptive
element analysis was validated using a case study of an operating airline. The
analysis indicates that potential disruptive elements in a schedule of an
airline are also actual disruptive elements in the historical data and they
should be considered to improve operations. The airline network exhibits
small-world effects and delays can propagate to any part of the network with a
minimum of four delayed flights. Finally, we observed that passenger
connections between flights are the most disruptive connection type. Therefore,
the proposed methodology provides a tool for airlines to build robust flight
schedules that reduce delays and propagation.
