Dynamic Placement in Refugee Resettlement

Abstract

Employment outcomes of resettled refugees depend strongly on where they are placed inside the host country. While the U.S. sets refugee capacities for communities on an annual basis, refugees arrive and must be placed over the course of the year. We introduce a dynamic allocation system based on potentials derived from dual prices of a linear programming (LP) relaxation to improve employment outcomes. Our algorithm achieves over 98% of the hindsight- optimal employment compared to under 90% 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.