Dynamic Placement in Refugee Resettlement
Narges Ahani, Paul Gölz, Ariel D. Procaccia, Alexander Teytelboym, Andrew C. Trapp
Operations Research · 2024 · Volume 72 · Issue 3
Abstract
Employment outcomes of resettled refugees depend strongly on where they are initially placed in the host country. Each week, a resettlement agency is allocated a set of refugees by the U.S. government. The agency must place these refugees in its local affiliates while respecting the affiliates’ annual capacities. We develop an allocation system that recommends where to place an incoming refugee family to improve total employment success. Our algorithm is based on two-stage stochastic programming and achieves over 98% of the hindsight-optimal employment, compared with 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 inseparable 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. Funding: This work was supported by the UK Economic and Social Research Council [Grant ES/R007470/1]; the Office of Naval Research [Grant N00014-20-1-2488]; and the National Science Foundation’s Division of Civil, Mechanical and Manufacturing Innovation [Grant CMMI-1825348], Division of Computing and Communication Foundations [Grants CCF-1733556 and CCF-2007080], Division of Mathematical Sciences [Grant DMS-1928930], and Division of Information and Intelligent Systems [Grant IIS-2024287]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2021.0534 .
Keywords
Policy Modeling and Public Sector OR, refugee resettlement, matching, stochastic optimization, integer optimization, humanitarian operations
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