Improving Access to Housing and Supportive Services for Runaway and Homeless Youth: Reducing Vulnerability to Human Trafficking in New York City

Abstract

Recent estimates indicate that there are over 1 million runaway and homeless youth and young adults (RHY) in the United States (US). Exposure to trauma, violence, and substance abuse, coupled with a lack of community support services, puts homeless youth at high risk of being exploited and trafficked. Although access to safe housing and supportive services such as physical and mental healthcare is an effective response to youth's vulnerability towards being trafficked, the number of youth experiencing homelessness exceeds the capacity of available housing resources in most US communities. We undertake an informed, systematic, and datadriven approach to project the collective capacity required by service providers to adequately meet the needs of homeless youth in New York City, including those most at risk of being trafficked. Our approach involves an integer linear programming model that extends the multiple multidimensional knapsack problem and is informed by partnerships with key stakeholders. The mathematical model allows for time-dependent allocation and capacity expansion, while incorporating stochastic youth arrivals and length of stays, services provided in periodic fashion and service delivery time windows. Our RHY and service provider-centered approach is an important step toward meeting the actual, rather than presumed, survival needs of vulnerable youth, particularly those at-risk of being trafficked.

Lagrangian Relaxation for Mixed-Integer Linear Programming: Importance, Challenges, Recent Advancements, and Opportunities

“…MILP has multiple applications in problems of importance to society: ambulance relocation (Lee et al, 2022), balanced item placement (Gasse et al, 2022), cost-sharing for ride-sharing (Hu et al, 2021), drop box location (Schmidt and Albert, 2022), efficient failure detection in large-scale distributed systems (Er-Rahmadi and Ma, 2022), home healthcare routing (Dastgoshade et al, 2020), home service routing and appointment scheduling (Tsang and Shehadeh, 2022), inventory control under demand and lead time uncertainty (Thorsen and Yao, 2017), job-shop scheduling (Liu et al, 2021), facility location (Basciftci et al, 2021), flow-shop scheduling (Hong et al, 2019;Balogh et al, 2022;Öztop et al, 2022), freight transportation (Archetti et al, 2021), location and inventory prepositioning of disaster relief supplies (Shehadeh and Tucker, 2022), machine scheduling with sequence-dependent setup times (Yalaoui and Nguyen, 2021), maritime inventory routing (Gasse et al, 2022), multi-agent path finding with conflict-based search (Huang et al, 2021), multi-depot electric bus scheduling (Gkiotsalitis et al, 2023), multi-echelon/multi-facility green reverse logistics network design (Reddy et al, 2022), optimal physician staffing (Prabhu et al, 2021), optimal search path with visibility (Morin et al, 2023), oral cholera vaccine distribution (Smalley et al, 2015), outpatient colonoscopy scheduling (Shehadeh et al, 2020), pharmaceutical distribution (Zhu and Ursavas, 2018), plant factory crop scheduling (Huang et al, 2020), post-disaster blood supply (Hamdan and Diabat, 2020;Kamyabniya et al, 2021), real assembly line balancing with human-robot collaboration (Nourmohammadi et al, 2022), reducing vulnerability to human trafficking (Kaya et al, 2022), restoration planning and crew routing (Morshedlou et al, 2021)...…”