  In this paper, we re-evaluate the basic strategies for label correcting
algorithms for the multiobjective shortest path (MOSP) problem, i.e., node and
label selection. In contrast to common believe, we show that---when carefully
implemented---the node-selection strategy usually beats the label-selection
strategy. Moreover, we present a new pruning method which is easy to implement
and performs very well on real-world road networks. In this study, we test our
hypotheses on artificial MOSP instances from the literature with up to 15
objectives and real-world road networks with up to almost 160,000 nodes.
