  Consider a problem in which $n$ jobs that are classified into $k$ types
arrive over time at their release times and are to be scheduled on a single
machine so as to minimize the maximum flow time. The machine requires a setup
taking $s$ time units whenever it switches from processing jobs of one type to
jobs of a different type. We consider the problem as an online problem where
each job is only known to the scheduler as soon as it arrives and where the
processing time of a job only becomes known upon its completion
(non-clairvoyance).
  We are interested in the potential of simple "greedy-like" algorithms. We
analyze a modification of the FIFO strategy and show its competitiveness to be
$\Theta(\sqrt{n})$, which is optimal for the considered class of algorithms.
For $k=2$ types it achieves a constant competitiveness. Our main insight is
obtained by an analysis of the smoothed competitiveness. If processing times
$p_j$ are independently perturbed to $\hat p_j = (1+X_j)p_j$, we obtain a
competitiveness of $O(\sigma^{-2} \log^2 n)$ when $X_j$ is drawn from a uniform
or a (truncated) normal distribution with standard deviation $\sigma$. The
result proves that bad instances are fragile and "practically" one might expect
a much better performance than given by the $\Omega(\sqrt{n})$-bound.
