Dynamic Placement in Refugee Resettlement

Narges Ahani (), Paul Gölz (), Ariel D. Procaccia (), Alexander Teytelboym () and Andrew C. Trapp ()
Additional contact information
Narges Ahani: Bank of America, Charlotte, North Carolina 28202
Paul Gölz: Simons Laufer Mathematical Sciences Institute, Berkeley, California 94720
Ariel D. Procaccia: School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138
Alexander Teytelboym: Department of Economics, University of Oxford, Oxford OX1 3UQ, United Kingdom
Andrew C. Trapp: WPI Business School and Data Science Program, Worcester Polytechnic Institute, Worcester, Massachusetts 01609

Operations Research, 2024, vol. 72, issue 3, 1087-1104

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.

Keywords: Policy Modeling and Public Sector OR; refugee resettlement; matching; stochastic optimization; integer optimization; humanitarian operations (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/opre.2021.0534 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:72:y:2024:i:3:p:1087-1104

Access Statistics for this article

More articles in Operations Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().