Placement Optimization in Refugee Resettlement

Research output: Working paper


Every year thousands of refugees are resettled to dozens of host countries. While there is growing evidence that the initial placement of refugee families profoundly affects their lifetime outcomes, there have been few attempts to optimize resettlement destinations. We integrate machine learning and integer optimization technologies into an innovative software tool that assists a resettlement agency in the United States with matching refugees to their initial placements. Our software suggests optimal placements while giving substantial autonomy for the resettlement staff to fine-tune recommended matches. Initial back-testing indicates that Annie can improve short-run employment outcomes by 22%-37%. We discuss several directions for future work such as incorporating multiple objectives from additional integration outcomes, dealing with equity concerns, evaluating potential new locations for resettlement, managing quota in a dynamic fashion, and eliciting refugee preferences.


External organisations
  • Worcester Polytechnic Institute
  • University of Oxford
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Economics


  • Refugee Resettlement, Matching, Integer Optimization, Machine Learning, Humanitarian Operations, C44, C55, C61, C78, F22, J61
Original languageEnglish
Number of pages36
Publication statusPublished - 2018
Publication categoryResearch

Publication series

NameWorking Papers
PublisherLund University, Department of Economics