| 1. |
- Ahani, Narges, et al.
(författare)
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Placement Optimization in Refugee Resettlement
- 2021
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Ingår i: Operations Research. - : Institute for Operations Research and the Management Sciences (INFORMS). - 0030-364X .- 1526-5463. ; 69:5, s. 1468-1486
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Tidskriftsartikel (refereegranskat)abstract
- Every year, tens of thousands of refugees are resettled to dozens of host countries. Although there is growing evidence that the initial placement of refugee families profoundly affects their lifetime outcomes, there have been few attempts to optimize resettlement decisions. We integrate machine learning and integer optimization into an innovative software tool, Annie™ Matching and Outcome Optimization for Refugee Empowerment (Annie™ Moore), that assists a U.S. resettlement agency with matching refugees to their initial placements. Our software suggests optimal placements while giving substantial autonomy to the resettlement staff to fine-tune recommended matches, thereby streamlining their resettlement operations. Initial back testing indicates that Annie™ can improve short-run employment outcomes by 22%–38%. We conclude by discussing several directions for future work.
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| 2. |
- Trapp, Andrew C., et al.
(författare)
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Placement Optimization in Refugee Resettlement
- 2018
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Annan publikation (övrigt vetenskapligt/konstnärligt)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.
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| 3. |
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| 4. |
- Trapp, Andrew C., et al.
(författare)
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Refugee resettlement via machine learning and integer optimization
- 2018
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Ingår i: OR60 : The OR Society Annual Conference - The OR Society Annual Conference. - 9780903440646 ; , s. 21-26
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Konferensbidrag (refereegranskat)abstract
- Around 100,000 refugees are resettled to dozens of countries from conflict zones every year. 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 within host countries. We describe how machine learning and integer optimization can be used to empower resettlement agencies to drastically improve refugee employment outcomes. We describe possible future work on multi-objective optimization, the dynamics of allocation, and the inclusion of refugee preferences.
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