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

Kaya, Yaren Bilge; Maass, Kayse Lee; Konrad, Renata; Trapp, Andrew C.; Dimas, Geri L.; Dank, Meredith

doi:10.1109/wsc57314.2022.10015303

Cited by 2 publications

(2 citation statements)

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“…In this chapter we briefly explain our data collection process and present our mixed integer linear program (MILP) model that projects the cost minimizing capacity to deploy to New York City shelters to meet runaway and homeless youth's collective needs [112].…”

Section: Capacity Expansion Of Til Organizationsmentioning

confidence: 99%

“…The application of operations research (OR) and analytics techniques can provide solutions to various challenges in public housing systems. Previous research related to OR and analytics efforts to reduce homelessness and improve access to housing resources largely focus on three broad areas: (i) location planning [105,103,143], (ii) capacity planning [113,114,158], and (iii) allocation mechanisms or routing strategies [107,40,24,17,181,110].…”

Section: Literature Reviewmentioning

confidence: 99%

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Operations research and analytics methods to improve equitable access to public services

Kaya

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brother, whose love and guidance are with me in whatever I pursue. Your unwavering support, patience, and care have made me the person I am today (and the Russian classics you made me read growing up).Last but not least, I want to express my deepest appreciation to my loving and supportive partner, Carl, who has always been there for the best and the worst to provide his unending inspiration. There were times knowing that he believed in me was my only motivation to keep trying. v TABLE OF CONTENTS ACKNOWLEDGMENTS iii LIST OF TABLES vii LIST OF FIGURES ix

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Section: Capacity Expansion Of Til Organizationsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Operations research and analytics methods to improve equitable access to public services

Kaya

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A Human-Centered Review of Algorithms in Homelessness Research

Moon,

Guha

2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Homelessness is a humanitarian challenge affecting an estimated 1.6 billion people worldwide. In the face of rising homeless populations in developed nations and a strain on social services, government agencies are increasingly adopting data-driven models to determine one's risk of experiencing homelessness and assigning scarce resources to those in need. We conducted a systematic literature review of 57 papers to understand the evolution of these decision-making algorithms. We investigated trends in computational methods, predictor variables, and target outcomes used to develop the models using a human-centered lens and found that only 9 papers (15.7%) investigated model fairness and bias. We uncovered tensions between explainability and ecological validity wherein predictive risk models (53.4%) unduly focused on reductive explainability while resource allocation models (25.9%) were dependent on unrealistic assumptions and simulated data that are not useful in practice. Further, we discuss research challenges and opportunities for developing human-centered algorithms in this area.CCS Concepts: • Human-centered computing → Human-computer interaction (HCI); Empirical studies in HCI ; • Applied computing → Computing in government.

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

Cited by 2 publications

References 20 publications

Operations research and analytics methods to improve equitable access to public services

Operations research and analytics methods to improve equitable access to public services

A Human-Centered Review of Algorithms in Homelessness Research

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