Placement Optimization in Refugee Resettlement

• 成果类型: Article
• 署名作者: Ahani, Narges; Andersson, Tommy; Martinello, Alessandro; Teytelboym, Alexander; Trapp, Andrew C.
• 署名单位: Worcester Polytechnic Institute; Lund University; University of Oxford; Worcester Polytechnic Institute
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2020.2093
• 发表日期: 2021
• 页码: 1468-1486
• 关键词: refugee resettlement; matching; integer optimization; Machine Learning; humanitarian operations

摘要

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 (TM) Matching and Outcome Optimization for Refugee Empowerment (Annie (TM) 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 (TM) can improve short-run employment outcomes by 22%-38%. We conclude by discussing several directions for future work.