Refugee resettlement via machine learning and integer optimization
(2018) 60th Annual Conference of the Operational Research Society, OR 2018 p.21-26- 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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/352e624e-f840-45ec-9f91-d6cda2c8fe3a
- author
- Trapp, Andrew C. ; Teytelboym, Alexander ; Ahani, Narges and Andersson, Tommy LU
- organization
- publishing date
- 2018
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Humanitarian operations research, Integer optimization, Machine learning, Matching, Multiple multidimensional knapsack problem, Refugees
- host publication
- OR60 : The OR Society Annual Conference - The OR Society Annual Conference
- editor
- Kheiri, Ahmed
- pages
- 6 pages
- publisher
- OR Society
- conference name
- 60th Annual Conference of the Operational Research Society, OR 2018
- conference location
- Lancaster, United Kingdom
- conference dates
- 2018-09-11 - 2018-09-13
- external identifiers
-
- scopus:85060549262
- ISBN
- 9780903440646
- language
- English
- LU publication?
- yes
- id
- 352e624e-f840-45ec-9f91-d6cda2c8fe3a
- date added to LUP
- 2019-02-11 08:31:31
- date last changed
- 2022-04-25 21:21:11
@inproceedings{352e624e-f840-45ec-9f91-d6cda2c8fe3a, abstract = {{<p>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.</p>}}, author = {{Trapp, Andrew C. and Teytelboym, Alexander and Ahani, Narges and Andersson, Tommy}}, booktitle = {{OR60 : The OR Society Annual Conference}}, editor = {{Kheiri, Ahmed}}, isbn = {{9780903440646}}, keywords = {{Humanitarian operations research; Integer optimization; Machine learning; Matching; Multiple multidimensional knapsack problem; Refugees}}, language = {{eng}}, pages = {{21--26}}, publisher = {{OR Society}}, title = {{Refugee resettlement via machine learning and integer optimization}}, year = {{2018}}, }