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Law enforcement interventions continue to be the primary mechanism used to identify offenders and illicit businesses involved in human trafficking, yet trafficking continues to be a thriving international operation. We explore alternative mechanisms to disrupt illicit operations and reduce victimization through labor trafficking supply chains using supply chain disruption theory. Using a case study approach to examine one federally prosecuted labor trafficking case in the agricultural sector, we (1) extend criminological concepts of disruption by identifying sources and methods of disruption and (2) inform criminal justice system responses by presenting novel methods of assessing effectiveness of anti-human trafficking policies and programs. 

Human trafficking, the commercial exploitation of individuals, is a gross violation of human rights; harming societies, economies, health and development. The related disciplines of Operations Research (OR) and Analytics are uniquely positioned to support trafficking prevention and intervention efforts by efficiently evaluating a plethora of decision alternatives and providing quantitative, actionable insights. As operations and analytical efforts in the counter-trafficking field emerge, it is imperative to grasp subtle, yet distinctive, nuances associated with human trafficking. This paper is intended to inform those practitioners working in the Operations and Analytics fields by highlighting key features of human trafficking activity. We grouped ten themes around two broad categories: (1) representation of human trafficking and (2) consideration of survivors and communities. These insights are derived from our collective experience in working in this area and substantiated by domain expertise. Based on these areas, we then suggest avenues for future work. 

We consider a new class of multi-period network interdiction problems, where interdiction and restructuring decisions are decided upon before the network is operated and implemented throughout the time horizon.We discuss how we apply this new problem to disrupting domestic sex trafficking networks, and introduce a variant where a second cooperating attacker has the ability to interdict victims and prevent the recruitment of prospective victims. This problem is modeled as a bilevel mixed integer linear program (BMILP), and is solved using column-and-constraint generation with partial information. We also simplify the BMILP when all interdictions are implemented before the network is operated. Modeling-based augmentations are proposed to significantly improve the solution time in a majority of instances tested. We apply our method to synthetic domestic sex trafficking networks, and discuss policy implications from our model. In particular, we show how preventing the recruitment of prospective victims may be as essential to disrupting sex trafficking as interdicting existing participants. 

The New York City (NYC) youth shelter system provides housing, counseling, and other support services to runaway and homeless youth and young adults (RHY). These resources reduce RHY's vulnerability to human trafficking, yet most shelters are unable to meet demand. This paper presents a Discrete Event Simulation (DES) model of a crisis-emergency and drop-in center for LGBTQ+ youth in NYC, which aims to analyze the current operations and test potential capacity expansion interventions. The model uses data from publicly available resources and interviews with service providers and key stakeholders. The simulated shelter has 66 crisis-emergency beds, offers five different support services, and serves on average 1,399 LGBTQ+ RHY per year. The capacity expansion interventions examined in this paper are adding crisis-emergency beds and psychiatric therapists. This application of DES serves as a tool to communicate with policymakers, funders, and service providers-potentially having a strong humanitarian impact. 

ISE tools, advisory group guidance applied to tackle global crisis 

Background: Human trafficking for sexual exploitation (referred to as sex trafficking) is a complex global challenge that causes harm and violates human rights. Most research has focused on victim-level harms and experiences, with limited understanding of the networks and business functions of trafficking operations. Empirical evidence is lacking on how to disrupt trafficking operations because it is difficult to study; it is hidden and dangerous, spans academic disciplinary boundaries, and necessitates ways of knowing that include lived experience. Collaborative approaches are needed, but there is limited research on methods to best build transdisciplinary teams. Aim: The aim of this study was to understand how to form a community-engaged transdisciplinary research team that combines qualitative and operations research with a survivor-centered advisory group. Methods: We conducted a qualitative meta-study of our team that is seeking to mathematically model sex trafficking operations. Data were collected from the minutes of 16 team meetings and a survey of 13 team members. Results: Analysis of meeting minutes surfaced four themes related to content and style of communication, one related to value statements, and one capturing intentional team building efforts. Survey results highlighted respect, trust, integrity, openness and asking and answering questions as key aspects of team building. Results show that an action research approach to team building, focused on trust and communication, fostered effective collaboration among social scientists, operations researchers, and survivors of trafficking. Conclusion: Team building, shared language, and trust are essential, yet often neglected, elements of team science. This meta-study provides important methodological insights on community engaged transdisciplinary team formation to tackle vexing social challenges.