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Dynamic Placement in Refugee Resettlement

Author

Listed:
  • 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)

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.

Suggested Citation

  • Narges Ahani & Paul Gölz & Ariel D. Procaccia & Alexander Teytelboym & Andrew C. Trapp, 2024. "Dynamic Placement in Refugee Resettlement," Operations Research, INFORMS, vol. 72(3), pages 1087-1104, May.
    • Handle: RePEc:inm:oropre:v:72:y:2024:i:3:p:1087-1104
    DOI: 10.1287/opre.2021.0534
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