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ISN2: Coordinated Interdiction for Disruption of Labor Trafficking in the Agricultural Sector

ISN2：协调拦截农业部门的劳动力贩运

基本信息

批准号：
1935618
负责人：
Kayse Maass
金额：
$ 57.41万
依托单位：
Northeastern University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-01-01 至 2024-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1935618&HistoricalAwards=false
关键词：
ISN2 Coordinated Interdiction Disruption Labor

项目摘要

This award will advance national health, welfare, and prosperity by furthering our understanding of effective operational methods to combat labor trafficking within the U.S. agricultural sector. Labor trafficking has been documented within supply chains that provide much of the food we consume and many of the products we use each day, with migrant labor workers being particularly vulnerable to exploitation. Thus far, the primary approach to combating human trafficking in the U.S. has been through crime control efforts by the criminal justice system (e.g. arrest, prosecution, incapacitation of offenders). However, prior research has identified limitations of this approach, particularly in addressing labor trafficking, which suggests additional mechanisms should be considered. This interdisciplinary research project exploits the knowledge of operational models and methods from commercial supply chain management to guide efforts to disrupt labor trafficking in the U.S. agricultural sector, focusing paticularly on infrastructure use, economic factors affecting supply and demand, and environmental factors, such as legal and regulatory frameworks. In addition, the project will study how a broad range of anti-trafficking stakeholders with limited resources can most efficiently coordinate their efforts to ensure that supply chains are free of trafficked labor. This research has the potential to transform the ways anti-trafficking policies and programs are formulated and evaluated, leading to more effective disruption of human trafficking supply chains within a limited resource environment. The project will support the work of two graduate students working at the interface of engineering, business analytics, and social science. This research will generate advances in network interdiction optimization and supply chain vulnerability theory while contributing to an emerging literature on operations engineering models that address illicit supply networks. Informed by data and perspectives from community partners and federally prosecuted labor trafficking cases in the U.S. agricultural sector, the research team will apply network analysis to qualitative labor trafficking typologies and identify key vulnerabilities in labor trafficking supply networks. To assess the impact of multiple anti-human trafficking stakeholders independently pursuing different tactics to disrupt labor trafficking, a multi-agent decentralized min-max flow interdiction model where interdictors operate on subgraphs of a common network will be developed. This decentralized approach will be compared with approaches in which anti-human trafficking stakeholders collaborate and coordinate their efforts. In the event that not all organizations and entities involved in anti-trafficking efforts are willing or able to coordinate, it will also serve as a basis for identifying which groups of decision makers and tactics are the most critical to creating impactful disruption.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.

该奖项将通过加深我们对打击美国农业部门劳工贩运的有效操作方法的理解，促进国家健康、福利和繁荣。在提供我们每天消费的大部分食物和许多产品的供应链中，已经记录了劳动力贩运，移民劳工特别容易受到剥削。到目前为止，美国打击人口贩运的主要方法是通过刑事司法系统的犯罪控制努力（例如逮捕、起诉、使罪犯丧失行为能力）。然而，先前的研究已经确定了这种方法的局限性，特别是在处理劳工贩运方面，这表明应该考虑其他机制。这个跨学科的研究项目利用商业供应链管理的运营模式和方法的知识来指导美国农业部门破坏劳动力贩运的努力，特别关注基础设施的使用，影响供需的经济因素，以及环境因素，如法律和监管框架。此外，该项目还将研究资源有限的广泛反贩运利益攸关方如何最有效地协调其努力，以确保供应链中没有被贩运的劳动力。这项研究有可能改变反贩运政策和计划的制定和评估方式，从而在有限的资源环境中更有效地破坏人口贩运供应链。该项目将支持两名研究工程、商业分析和社会科学的研究生的工作。这项研究将在网络拦截优化和供应链脆弱性理论方面取得进展，同时为解决非法供应网络的操作工程模型的新兴文献做出贡献。根据社区合作伙伴的数据和观点，以及美国农业部门联邦起诉的劳工贩运案件，研究小组将应用网络分析对定性的劳工贩运类型学进行分析，并确定劳动力贩运供应网络中的关键漏洞。为了评估多个反人口贩运利益相关者独立采取不同策略来破坏劳动力贩运的影响，将开发一个多代理分散最小-最大流量拦截模型，其中拦截器在共同网络的子图上操作。这种分散的做法将与打击人口贩运利益攸关方合作和协调其努力的做法进行比较。如果并非所有参与打击贩运活动的组织和实体都愿意或有能力进行协调，它也可以作为确定哪些决策者群体和策略对造成有影响的破坏最为关键的基础。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。