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Placement Optimization in Refugee Resettlement

Trapp , Andrew C. ; Teytelboym , Alexander ; Martinello, Alessandro LU ; Andersson, Tommy LU and Ahani, Narges (2018) In Working Papers
Abstract
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Working Paper
publication status
published
subject
keywords
Refugee Resettlement, Matching, Integer Optimization, Machine Learning, Humanitarian Operations, C44, C55, C61, C78, F22, J61
in
Working Papers
issue
2018:23
pages
36 pages
language
English
LU publication?
yes
id
b603b044-5612-463f-9edf-350acbeec269
alternative location
https://swopec.hhs.se/lunewp/abs/lunewp2018_023.htm
date added to LUP
2018-10-03 13:27:32
date last changed
2018-11-21 21:41:58
@misc{b603b044-5612-463f-9edf-350acbeec269,
  abstract     = {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. },
  author       = {Trapp , Andrew C.   and Teytelboym , Alexander  and Martinello, Alessandro and Andersson, Tommy and Ahani, Narges  },
  keyword      = {Refugee Resettlement,Matching,Integer Optimization,Machine Learning,Humanitarian Operations,C44,C55,C61,C78,F22,J61},
  language     = {eng},
  note         = {Working Paper},
  number       = {2018:23},
  pages        = {36},
  series       = {Working Papers},
  title        = {Placement Optimization in Refugee Resettlement},
  year         = {2018},
}