Makespan minimization on identical parallel machines is a classical
scheduling problem. We consider the online scenario where a sequence of $n$
jobs has to be scheduled non-preemptively on $m$ machines so as to minimize the
maximum completion time of any job. The best competitive ratio that can be
achieved by deterministic online algorithms is in the range $[1.88,1.9201]$.
Currently no randomized online algorithm with a smaller competitiveness is
known, for general $m$.
In this paper, we consider the \alg{Generalized Bin Covering} problem: We are
given $m$ bin types, where each bin of type $i$ has profit $p_i$ and demand
$d_i$. Furthermore, there are $n$ items, where item $j$ has size $s_j$. A bin
of type $i$ is covered if the set of items assigned to it has total size at
least the demand $d_i$. In that case, the profit of $p_i$ is earned and the
objective is to maximize the total profit. To the best of our knowledge, only
the cases $p_i = d_i = 1$ (\alg{Bin Covering}) and $p_i = d_i$
(\alg{Variable-Sized Bin Covering}) have been treated before.