  The NP-hard scheduling problem P||C_max encompasses a set of tasks with known
execution time which must be mapped to a set of identical machines such that
the overall completion time is minimized. In this work, we improve existing
techniques for optimal P||C_max scheduling with a combination of new
theoretical insights and careful practical engineering. Most importantly, we
derive techniques to prune vast portions of the search space of
branch-and-bound (BnB) approaches. We also propose improved upper and lower
bounding techniques which can be combined with any approach to P||C_max.
Moreover, we present new benchmarks for P||C_max, based on diverse application
data, which can shed light on aspects which prior synthetic instances fail to
capture. In an extensive evaluation, we observe that our pruning techniques
reduce the number of explored nodes by 90$\times$ and running times by
12$\times$. Compared to a state-of-the-art ILP-based approach, our approach is
preferable for short running time limits and for instances with large
makespans.
