  We consider a system where a human operator processes a sequence of tasks
that are similar in nature under a total time constraint. In these systems, the
performance of the operator depends on its past utilization. This is akin to
$\textit{state-dependent}$ channels where the past actions of the transmitter
affects the future quality of the channel (also known as
$\textit{action-dependent}$ or $\textit{use-dependent}$ channels). For
$\textit{human channels}$, a well-known psychological phenomena, known as
$\textit{Yerkes-Dodson law}$, states that a human operator performs worse when
he/she is over-utilized or under-utilized. Over such a $\textit{use-dependent}$
human channel, we consider the problem of maximizing a utility function, which
is monotonically increasing and concave in the time allocated for each task,
under explicit minimum and maximum $\textit{utilization}$ constraints. We show
that the optimal solution is to keep the utilization ratio of the operator as
high as possible, and to process all the tasks. We prove that the optimal
policy consists of two major strategies: utilize the operator without resting
until reaching the maximum allowable utilization ratio, and then alternate
between working and resting the operator each time reaching the maximum
allowable utilization at the end of work-period. We show that even though the
tasks are similar in difficulty, the time allocated for the tasks can be
different depending on the strategy in which a task is processed; however, the
tasks processed in the same strategy are processed equally.
