  Unscheduled maintenance has contributed to longer downtime for vehicles and
increased costs for Logistic Readiness Squadrons (LRSs) in the Air Force. When
vehicles are in need of repair outside of their scheduled time, depending on
their priority level, the entire squadron's slated repair schedule is
transformed negatively. The repercussions of unscheduled maintenance are
specifically seen in the increase of man hours required to maintain vehicles
that should have been working well: this can include more man hours spent on
maintenance itself, waiting for parts to arrive, hours spent re-organizing the
repair schedule, and more. The dominant trend in the current maintenance system
at LRSs is that they do not have predictive maintenance infrastructure to
counteract the influx of unscheduled repairs they experience currently, and as
a result, their readiness and performance levels are lower than desired.
  We use data pulled from the Defense Property and Accountability System
(DPAS), that the LRSs currently use to store their vehicle maintenance
information. Using historical vehicle maintenance data we receive from DPAS, we
apply three different algorithms independently to construct an accurate
predictive system to optimize maintenance schedules at any given time. Through
the application of Logistics Regression, Random Forest, and Gradient Boosted
Trees algorithms, we found that a Logistic Regression algorithm, fitted to our
data, produced the most accurate results. Our findings indicate that not only
would continuing the use of Logistic Regression be prudent for our research
purposes, but that there is opportunity to further tune and optimize our
Logistic Regression model for higher accuracy.
