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

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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.