  Reliable platforms for data collation during airline schedule operations have
significantly increased the quality and quantity of available information for
effectively managing airline schedule disruptions. To that effect, this paper
applies macroscopic and microscopic techniques by way of basic statistics and
machine learning, respectively, to analyze historical scheduling and operations
data from a major airline in the United States. Macroscopic results reveal that
majority of irregular operations in airline schedule that occurred over a
one-year period stemmed from disruptions due to flight delays, while
microscopic results validate different modeling assumptions about key drivers
for airline disruption management like turnaround as a Gaussian process.
