Dynamic Placement in Refugee Resettlement

• 成果类型: Article
• 署名作者: Ahani, Narges; Golz, Paul; Procaccia, Ariel D.; Teytelboym, Alexander; Trapp, Andrew C.
• 署名单位: Bank of America Corporation; Harvard University; University of Oxford; Worcester Polytechnic Institute; Worcester Polytechnic Institute
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2021.0534
• 发表日期: 2024
• 页码: 1087-1104
• 关键词: integration; assignment

摘要

Employment outcomes of resettled refugees depend strongly on where they are initially placed in the host country. Each week, a resettlement agency is allocated a set of refugees by the U.S. government. The agency must place these refugees in its local affiliates while respecting the affiliates' annual capacities. We develop an allocation system that recommends where to place an incoming refugee family to improve total employment success. Our algorithm is based on two-stage stochastic programming and achieves over 98% of the hindsight-optimal employment, compared with under 90% 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 inseparable families, batching, and uncertainty with respect to the number of future arrivals. Our algorithm is now part of the AnnieTM MOORE optimization software used by a leading American refugee resettlement agency.