arXiv subjects

Andrew C. Trapp

Publications and source records attributed to Andrew C. Trapp.

13 recordsLinked to original sources

arXiv2023

Optimizing Sponsored Humanitarian Parole

The United States has introduced a special humanitarian parole process for Ukrainian citizens in response to Russia 2022 invasion of Ukraine. To qualify for parole, Ukrainian applicants must have a sponsor in the United States. In collaboration with HIAS, a refugee resettlement agency involved in the parole process, we deployed RUTH (Refugees Uniting Through HIAS), a novel algorithmic matching system that is driven by the relocation preferences of refugees and the priorities of US sponsors. RUTH adapts Thakral Multiple-Waitlist Procedure (MWP) that combines the main FIFO queue with location-specific FIFO queues to effectively manage the preferences of refugees and the supply of community sponsors. RUTH also incorporates various feasibility considerations, such as community capacity religious, and medical needs. The adapted mechanism is envy-free, efficient, and strategy-proof for refugees. Our analysis shows diverse refugee location preferences, not fully explained by observables, highlighting the challenge of predicting preferences directly from them. We use our data for two counterfactual simulations. First, we consider the effects of increased waiting times for refugees on the quality of their matches. We find that with a periodic Top Trading Cycles algorithm, increasing period length from 24 days to 80 days improves the average rank of a refugees match from 3.20 to 2.44. Second, we estimate the arrival rates of sponsors in each location that would be consistent with a long-run steady state. We find that more desirable locations (regarding refugee preferences) require the highest arrival rates, suggesting that preferences might be a useful indicator for investments in sponsorship capacity. Our study highlights the potential for preference-based algorithms such as RUTH to improve the efficiency and fairness of other rapidly deployed humanitarian parole processes.

physics.soc-ph↗

arXiv2023

DiversiTree: A New Method to Efficiently Compute Diverse Sets of Near-Optimal Solutions to Mixed-Integer Optimization Problems

While most methods for solving mixed-integer optimization problems compute a single optimal solution, a diverse set of near-optimal solutions can often lead to improved outcomes. We present a new method for finding a set of diverse solutions by emphasizing diversity within the search for near-optimal solutions. Specifically, within a branch-and-bound framework, we investigated parameterized node selection rules that explicitly consider diversity. Our results indicate that our approach significantly increases the diversity of the final solution set. When compared with two existing methods, our method runs with similar runtime as regular node selection methods and gives a diversity improvement between 12% and 190%. In contrast, popular node selection rules, such as best-first search, in some instances performed worse than state-of-the-art methods by more than 35% and gave an improvement of no more than 130%. Further, we find that our method is most effective when diversity in node selection is continuously emphasized after reaching a minimal depth in the tree and when the solution set has grown sufficiently large. Our method can be easily incorporated into integer programming solvers and has the potential to significantly increase the diversity of solution sets.

cs.DM↗

arXiv2022

Perspectives on How To Conduct Responsible Anti-Human Trafficking Research in Operations and Analytics

Human trafficking, the commercial exploitation of individuals, is a gross violation of human rights and harms societies, economies, health and development. The related disciplines of Operations Management (OM), Analytics, and Operations Research (OR) 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 note is intended to inform those practitioners working in the field by highlighting key features of human trafficking activity. We grouped nine themes around three broad categories: (1) representation of human trafficking, (2) consideration of survivors and communities, and (3) analytics related. These insights are derived from our collective experience in working in this area and substantiated by domain expertise.

cs.CY↗

arXiv2022

On the Optimization of Benefit to Cost Ratios for Public Sector Decision Making

Decision making in the public sector centers on delivering resources and services for the common good, emphasizing an expansive set of objectives such as equity and efficiency, beyond immediate short term returns to reflect the broader cares of society and public beneficiaries. Cost-benefit analysis is a prevailing decision-making framework in the public sector that often uses the benefit to cost ratio (BCR) to compare viable alternatives, yet no systematic framework exists for evaluating many alternatives beyond the status quo of doing nothing. We propose a new framework to maximize the BCR for public sector decisions, seeking the largest improvement per marginal deployment of capacity. Requiring a status quo representable through (constrained) decision variables, the framework is generally applicable and useful to a broad set of decision contexts that involve maximizing the BCR for marginal deployments of resources. We demonstrate the applicability of our framework on a compelling case study for the New York City runaway and homeless youth shelter system, an area of high societal need. We represent this problem as a mixed integer linear fractional program (MILFP) and employ Dinkelbach's algorithm that converts the MILFP to a series of linearized mixed-integer optimization problems, making our approach tractable for fairly large problem instances. Our optimization-based algorithmic framework yields data-informed recommendations for making New York City shelter expansion decisions to better serve runaway and homeless youth, and generalizes to reveal managerial insights for optimizing the BCR. More broadly, our algorithmic decision making framework allows for iteration and comparison across multiple potential constraints ensuring action away from the status quo, thereby empowering effective assessment of marginal deployment of additional resources.

math.OC↗

arXiv2022

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

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 youths vulnerability towards being trafficked, the number of youth experiencing homelessness exceeds the capacity of available housing resources in most US communities. We undertake a RHY-informed, systematic, and data driven 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 a 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.

math.OC↗

arXiv2022

Discrete Event Simulation to Evaluate Shelter Capacity Expansion Options for LGBTQ+ Homeless Youth

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 RHYs 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.

math.NA↗

arXiv2022

Estimating Effectiveness of Identifying Human Trafficking via Data Envelopment Analysis

Transit monitoring is a preventative approach used to identify possible cases of human trafficking prior to exploitation while an individual is in transit or before one crosses a border. Transit monitoring is often conducted by non-governmental organizations (NGOs) who train staff to identify and intercept suspicious activity. Love Justice International (LJI) is a well-established NGO that has been conducting transit monitoring for years along the Nepal-India border at multiple monitoring stations. In partnership with LJI, we developed a system that uses data envelopment analysis (DEA) to help LJI decision-makers evaluate the performance of these stations at intercepting potential human-trafficking victims given the amount of resources (e.g. staff, etc.) available and make specific operational improvement recommendations. Our model consists of 91 decision-making units (DMUs) from 7 stations over 13 quarters and considers three inputs, four outputs, and 3 homogeneity criteria. Using this model we identified efficient stations, compared rankings of station performance, and recommended strategies to improve efficiency. To the best of our knowledge, this is the first application of DEA in the anti-human trafficking domain.

cs.CY↗

arXiv2022

Dynamic Placement in Refugee Resettlement

Employment outcomes of resettled refugees depend strongly on where they are placed inside the host country. Each week, a resettlement agency is assigned a batch of refugees by the United States government. The agency must place these refugees in its local affiliates, while respecting the affiliates' yearly capacities. We develop an allocation system that suggests where to place an incoming refugee, in order to improve total employment success. Our algorithm is based on two-stage stochastic programming and achieves over 98 percent of the hindsight-optimal employment, compared to under 90 percent of current greedy-like approaches. This dramatic improvement persists even when we incorporate a vast array of practical features of the refugee resettlement process including indivisible families, batching, and uncertainty with respect to the number of future arrivals. Our algorithm is now part of the Annie MOORE optimization software used by a leading American refugee resettlement agency.

cs.GT↗

arXiv2022

Operations Research and Analytics to Combat Human Trafficking: A Systematic Review of Academic Literature

Human trafficking is a widespread and compound social, economic, and human rights issue occurring in every region of the world. While there have been an increasing number of anti-human trafficking works from the Operations Research and Analytics domains in recent years, no systematic review of this literature currently exists. We fill this gap by providing a systematic literature review that identifies and classifies the body of Operations Research and Analytics research related to the anti-human trafficking domain, thereby illustrating the collective impact of the field to date. We classify 142 studies to identify current trends in methodologies, theoretical approaches, data sources, trafficking contexts, target regions, victim-survivor demographics, and focus within the well-established 4Ps principles. Using these findings, we discuss the extent to which the current literature aligns with the global demographics of human trafficking and identify existing research gaps to propose an agenda for Operations Research and Analytics researchers.

cs.CY↗

arXiv2022

Optimization for Secure and Humane Border Operations

In 2020 tens of millions of migrants worldwide sought safety due to various push-pull factors such as economic, geographic, demographic, and societal challenges. Large movements of human flows toward national borders constitute a complex humanitarian crisis that requires appropriate preparations of border resources. Motivated by prevailing national agenda issues for improved border support, we identify common operational challenges along the border and discuss how they can be addressed using advanced analytics. Multiple stakeholders, corresponding goals, and complex decisions call for advanced optimization techniques to holistically treat such challenges. This chapter highlights how optimization can be employed to improve both the security and humanitarian aspects of border operations in the context of migration flows.

math.OC↗

arXiv2021

Human-Centric Decision Support Tools: Insights from Real-World Design and Implementation

Decision support tools enable improved decision-making for challenging decision problems by empowering stakeholders to process, analyze, visualize, and otherwise make sense of a variety of key factors. Their intentional design is a critical component of the value they create. All decision-support tools share in common that there is a complex decision problem to be solved for which decision-support is useful, and moreover, that appropriate analytics expertise is available to produce solutions to the problem setting at hand. When well-designed, decision support tools reduce friction and increase efficiency in providing support for the decision-making process, thereby improving the ability of decision-makers to make quality decisions. On the other hand, the presence of overwhelming, superfluous, insufficient, or ill-fitting information and software features can have an adverse effect on the decision-making process and, consequently, outcomes. We advocate for an innovative, and perhaps overlooked, approach to designing effective decision support tools: genuinely listening to the project stakeholders, to ascertain and appreciate their real needs and perspectives. By prioritizing stakeholder needs, a foundation of mutual trust and understanding is established with the design team. We maintain this trust is critical to eventual tool acceptance and adoption, and its absence jeopardizes the future use of the tool, which would leave its analytical insights for naught. We discuss examples across multiple contexts to underscore our collective experience, highlight lessons learned, and present recommended practices to improve the design and eventual adoption of decision dupport tools.

cs.CY↗

arXiv2020

MAP Clustering under the Gaussian Mixture Model via Mixed Integer Nonlinear Optimization

We present a global optimization approach for solving the maximum a-posteriori (MAP) clustering problem under the Gaussian mixture model.Our approach can accommodate side constraints and it preserves the combinatorial structure of the MAP clustering problem by formulating it asa mixed-integer nonlinear optimization problem (MINLP). We approximate the MINLP through a mixed-integer quadratic program (MIQP) transformation that improves computational aspects while guaranteeing $ε$-global optimality. An important benefit of our approach is the explicit quantification of the degree of suboptimality, via the optimality gap, en route to finding the globally optimal MAP clustering. Numerical experiments comparing our method to other approaches show that our method finds a better solution than standard clustering methods. Finally, we cluster a real breast cancer gene expression data set incorporating intrinsic subtype information; the induced constraints substantially improve the computational performance and produce more coherent and bio-logically meaningful clusters.

stat.ML↗

arXiv2017

A Deterministic Global Optimization Method for Variational Inference

Variational inference methods for latent variable statistical models have gained popularity because they are relatively fast, can handle large data sets, and have deterministic convergence guarantees. However, in practice it is unclear whether the fixed point identified by the variational inference algorithm is a local or a global optimum. Here, we propose a method for constructing iterative optimization algorithms for variational inference problems that are guaranteed to converge to the $ε$-global variational lower bound on the log-likelihood. We derive inference algorithms for two variational approximations to a standard Bayesian Gaussian mixture model (BGMM). We present a minimal data set for empirically testing convergence and show that a variational inference algorithm frequently converges to a local optimum while our algorithm always converges to the globally optimal variational lower bound. We characterize the loss incurred by choosing a non-optimal variational approximation distribution suggesting that selection of the approximating variational distribution deserves as much attention as the selection of the original statistical model for a given data set.

stat.ME↗