  Employment outcomes of resettled refugees depend strongly on where they are
placed inside the host country. Each week, a resettlement agency is assigned a
batch of refugees by the United States government. The agency must place these
refugees in its local affiliates, while respecting the affiliates' yearly
capacities. We develop an allocation system that suggests where to place an
incoming refugee, in order to improve total employment success. Our algorithm
is based on two-stage stochastic programming and achieves over 98 percent of
the hindsight-optimal employment, compared to under 90 percent of current
greedy-like approaches. This dramatic improvement persists even when we
incorporate a vast array of practical features of the refugee resettlement
process including indivisible families, batching, and uncertainty with respect
to the number of future arrivals. Our algorithm is now part of the Annie MOORE
optimization software used by a leading American refugee resettlement agency.
