  The decision to rollout a vehicle is critical to fleet management companies
as wrong decisions can lead to additional cost of maintenance and failures
during journey. With the availability of large amount of data and advancement
of machine learning techniques, the rollout decisions of a supervisor can be
effectively automated and the mistakes in decisions made by the supervisor
learnt. In this paper, we propose a novel learning algorithm SUPAID which under
a natural 'one-way efficiency' assumption on the supervisor, uses a rule mining
approach to rank the vehicles based on their roll-out feasibility thus helping
prevent the supervisor from makingerroneous decisions. Our experimental results
on real data from a public transit agency from a city in U.S show that the
proposed method SUPAID can result in significant cost savings.
