  Application of UAV in indoor environment is emerging nowadays due to the
advancements in technology. UAV brings more space-flexibility in an occupied or
hardly-accessible indoor environment, e.g., shop floor of manufacturing
industry, greenhouse, nuclear powerplant. UAV helps in creating an autonomous
manufacturing system by executing tasks with less human intervention in
time-efficient manner. Consequently, a scheduler is one essential component to
be focused on; yet the number of reported studies on UAV scheduling has been
minimal. This work proposes a methodology with a heuristic (based on Earliest
Available Time algorithm) which assigns tasks to UAVs with an objective of
minimizing the makespan. In addition, a quick response towards uncertain events
and a quick creation of new high-quality feasible schedule are needed. Hence,
the proposed heuristic is incorporated with Particle Swarm Optimization (PSO)
algorithm to find a quick near optimal schedule. This proposed methodology is
implemented into a scheduler and tested on a few scales of datasets generated
based on a real flight demonstration. Performance evaluation of scheduler is
discussed in detail and the best solution obtained from a selected set of
parameters is reported.
