Matching Problems in Refugee Resettlement

难民安置中的匹配问题

• 批准号: 1825348
• 负责人: Andrew Trapp
• 金额: $ 32.04万
• 依托单位: Worcester Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1825348&HistoricalAwards=false
• 关键词: Matching; Problems; Refugee; Resettlement

This project will promote the progress of science and contribute to the national prosperity and welfare by advancing analytical decision tools tackling the operational challenges of refugee resettlement in the United States. The goal of resettlement is to progressively integrate refugees into host societies, while balancing limitations of communities with the needs of refugees. This research will augment current manual refugee resettlement decision-making by using analytical methods that include machine learning and mathematical optimization. These technologies will improve humanitarian decision-making and have the potential to transform how domestic and worldwide resettlement decisions are made. Host communities will benefit by integrating refugees that bring new skills, youth and diversity to the matched communities. An established collaboration with a US-based refugee organization will guide the research and enable validation of the developed models in a real-world setting.This project will make two main methodological contributions. First, it will explore the algebraic and geometric properties of integral monoids to better understand and capitalize on their structure. It is believed that integral monoids can excel in contexts with flexible capacity and multiple objectives. This research will develop appropriate algorithms and data structures to efficiently and judiciously encode and retrieve monoid information, and will include algorithmic analyses to ensure the computational tractability. If successful, this research will contribute to new advances in optimization methodology, specifically the algorithmic use of integral monoids to solve other hard linear and nonlinear matching, knapsack, generalized assignment, and packing problems. Second, this research will construct novel objective functions by leveraging supervised machine learning techniques on existing refugee placement and integration (outcome) data. These new objective functions will guide the search toward more successful resettlement outcomes. The predictive modeling will also reveal previously undiscovered insights into how demographic and regional factors contribute to refugee integration.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将通过推进分析决策工具来应对美国难民安置的运营挑战，从而促进科学的进步，并为国家的繁荣和福利做出贡献。重新安置的目的是逐步将难民纳入接待社会，同时平衡社区的限制与难民的需求。这项研究将通过使用包括机器学习和数学优化的分析方法来增强当前的手动难民重新安置决策。这些技术将改善人道主义决策，并有可能改变国内和全球安置决策的方式。接待社区将通过整合为匹配社区带来新技能，青年和多样性的难民而受益。与美国的难民组织建立的合作将指导研究并能够在现实世界中验证开发的模型。该项目将作出两个主要的方法论贡献。首先，它将探索积分单体的代数和几何特性，以更好地理解和利用其结构。据认为，整体单体可以在具有灵活能力和多个目标的上下文中表现出色。这项研究将开发出适当的算法和数据结构，以有效，明智地编码和检索单体信息，并包括算法分析以确保计算障碍。如果成功，这项研究将有助于优化方法的新进步，特别是对积分单体的算法使用来解决其他硬性线性和非线性匹配，背包，广义分配和包装问题。其次，这项研究将通过利用监督的机器学习技术来构建新的目标功能，以实现现有的难民安置和整合（结果）数据。这些新的目标功能将指导搜索更成功的重新安置结果。预测建模还将揭示先前未被发现的见解，即对人口和区域因素如何对难民融合的贡献。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估标准来通过评估来支持的。

项目成果

Dynamic Placement in Refugee Resettlement

难民安置中的动态安置

• 发表时间: 2023
• 期刊: Operations research
• 影响因子: 2.7
• 作者: Narges Ahani, Paul Gölz

Placement Optimization in Refugee Resettlement

• DOI: 10.1287/opre.2020.2093
• 发表时间: 2021-01-01
• 期刊: PREPRINT
• 作者: Ahani, N.;Andersson, T.;Trapp, AC.
• 通讯作者: Trapp, AC.

Aid Allocation for Camp‐Based and Urban Refugees with Uncertain Demand and Replenishments

需求和补充不确定的难民营和城市难民的援助分配

• DOI: 10.1111/poms.13531
• 发表时间: 2021
• 期刊: Production and Operations Management
• 影响因子: 5
• 作者: Azizi, Shima;Bozkir, Cem Deniz;Trapp, Andrew C.;Kundakcioglu, O. Erhun;Kurbanzade, Ali Kaan
• 通讯作者: Kurbanzade, Ali Kaan