  Branch and bound algorithms have to cope with several additional difficulties
in the multi-objective case. Not only the bounding procedure is considerably
weaker, but also the handling of upper and lower bound sets requires much more
computational effort since both sets can be of exponential size. Thus, the
order in which the subproblems are considered is of particular importance.
Thereby, it is crucial not only to find efficient solutions as soon as possible
but also to find a set of (efficient) solutions whose images are well
distributed along the non-dominated frontier. In this paper we evaluate the
performance of multi-objective branch and bound algorithms depending on
branching and queuing of subproblems. We use, e.g., the hypervolume indicator
as a measure for the gap between lower and upper bound set to implement a
multi-objective best-first strategy. We test our approaches on multi-objective
knapsack and generalized assignment problems.
