  The Multi-Objective Multi-Agent Path Finding (MO-MAPF) problem is the problem
of finding the Pareto-optimal frontier of collision-free paths for a team of
agents while minimizing multiple cost metrics. Examples of such cost metrics
include arrival times, travel distances, and energy consumption.In this paper,
we focus on the Multi-Objective Conflict-Based Search (MO-CBS) algorithm, a
state-of-the-art MO-MAPF algorithm. We show that the standard splitting
strategy used by MO-CBS can lead to duplicate search nodes and hence can
duplicate the search effort that MO-CBS needs to make. To address this issue,
we propose two new splitting strategies for MO-CBS, namely cost splitting and
disjoint cost splitting. Our theoretical results show that, when combined with
either of these two new splitting strategies, MO-CBS maintains its completeness
and optimality guarantees. Our experimental results show that disjoint cost
splitting, our best splitting strategy, speeds up MO-CBS by up to two orders of
magnitude and substantially improves its success rates in various settings.
