  Disruption management during the airline scheduling process can be
compartmentalized into proactive and reactive processes depending upon the time
of schedule execution. The state of the art for decision-making in airline
disruption management involves a heuristic human-centric approach that does not
categorically study uncertainty in proactive and reactive processes for
managing airline schedule disruptions. Hence, this paper introduces an
uncertainty transfer function model (UTFM) framework that characterizes
uncertainty for proactive airline disruption management before schedule
execution, reactive airline disruption management during schedule execution,
and proactive airline disruption management after schedule execution to enable
the construction of quantitative tools that can allow an intelligent agent to
rationalize complex interactions and procedures for robust airline disruption
management. Specifically, we use historical scheduling and operations data from
a major U.S. airline to facilitate the development and assessment of the UTFM,
defined by hidden Markov models (a special class of probabilistic graphical
models) that can efficiently perform pattern learning and inference on portions
of large data sets. We employ the UTFM to assess two independent and separately
disrupted flight legs from the airline route network. Assessment of a flight
leg from Dallas to Houston, disrupted by air traffic control hold for bad
weather at Dallas, revealed that proactive disruption management for turnaround
in Dallas before schedule execution is impractical because of zero transition
probability between turnaround and taxi-out.
