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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2105.14388 |
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| _version_ | 1866910436375396352 |
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| author | Ahani, Narges Gölz, Paul Procaccia, Ariel D. Teytelboym, Alexander Trapp, Andrew C. |
| author_facet | Ahani, Narges Gölz, Paul Procaccia, Ariel D. Teytelboym, Alexander Trapp, Andrew C. |
| contents | Employment outcomes of resettled refugees depend strongly on where they are placed inside the host country. Each week, a resettlement agency is assigned a batch of refugees by the United States government. The agency must place these refugees in its local affiliates, while respecting the affiliates' yearly capacities. We develop an allocation system that suggests where to place an incoming refugee, in order to improve total employment success. Our algorithm is based on two-stage stochastic programming and achieves over 98 percent of the hindsight-optimal employment, compared to under 90 percent 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 indivisible 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2105_14388 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Dynamic Placement in Refugee Resettlement Ahani, Narges Gölz, Paul Procaccia, Ariel D. Teytelboym, Alexander Trapp, Andrew C. Computer Science and Game Theory Physics and Society Employment outcomes of resettled refugees depend strongly on where they are placed inside the host country. Each week, a resettlement agency is assigned a batch of refugees by the United States government. The agency must place these refugees in its local affiliates, while respecting the affiliates' yearly capacities. We develop an allocation system that suggests where to place an incoming refugee, in order to improve total employment success. Our algorithm is based on two-stage stochastic programming and achieves over 98 percent of the hindsight-optimal employment, compared to under 90 percent 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 indivisible 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. |
| title | Dynamic Placement in Refugee Resettlement |
| topic | Computer Science and Game Theory Physics and Society |
| url | https://arxiv.org/abs/2105.14388 |