  The Swiss State Secretariat for Migration recently announced a pilot project
for a machine learning-based assignment process for refugee resettlement. This
approach has the potential to substantially increase the overall employment
rate of refugees in Switzerland. However, the currently proposed method ignores
families' preferences. In this paper, we build on this prior work and propose
two matching mechanisms that additionally take families' preferences over
locations into account. The first mechanism is strategyproof while the second
is not but achieves higher family welfare. Importantly, we parameterize both
mechanisms, giving placement officers precise control how to trade off family
welfare against overall employment success. Preliminary simulations on
synthetic data show that both mechanisms can significantly increase family
welfare even with only a small loss on the overall employment rate of refugees.
