G. Norris, A. Qureshi, Katelyn Russo, Mariana Santander Gomez
{"title":"Analyzing Homeless Service Systems in Local Government Using a Systems Engineering Framework","authors":"G. Norris, A. Qureshi, Katelyn Russo, Mariana Santander Gomez","doi":"10.1109/SIEDS52267.2021.9483721","DOIUrl":null,"url":null,"abstract":"This paper investigates efficiency improvement opportunities within homeless service systems in the United States through modeling and simulation. Homeless service systems in the United States continue to evolve but are challenged by facility capacity and operational constraints. In this paper, a Maryland county homeless service system is selected as the case study for analysis. Data is collected through personnel interviews, Housing and Urban Development (HUD) data, and summarized annual reports from the client. Using a regression analysis model, key variables in flow-rates to stable housing solutions are determined in order to construct a system dynamics model of the homeless service system. This model is run for a period of 2 years, using the simulation software Vensim to identify bottlenecks as potential areas of improvement within the system. Model success is defined by HUD system performance measures, such as the length of time persons remain homeless and the rates at which persons placed in stable housing solutions return to homelessness. The model is further evaluated with the findings from a directed literature search of related case studies, semi-structured interviews with industry personnel, and a comparison to national best practices. The model will be generalized to simulate the HUD system performance measures of other homeless service systems in the United States. Additionally, the model will inform recommendations of identified improvements, such as altering ratios of case managers to facility occupants, modifying the intake assessment process, and optimizing facility programs for improved client flow. These recommendations will be applied to create a prototype dashboard for the client. This dashboard will be used as a forecasting tool to aid in decision-making affecting the operation of local homeless service systems.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 Systems and Information Engineering Design Symposium (SIEDS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIEDS52267.2021.9483721","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper investigates efficiency improvement opportunities within homeless service systems in the United States through modeling and simulation. Homeless service systems in the United States continue to evolve but are challenged by facility capacity and operational constraints. In this paper, a Maryland county homeless service system is selected as the case study for analysis. Data is collected through personnel interviews, Housing and Urban Development (HUD) data, and summarized annual reports from the client. Using a regression analysis model, key variables in flow-rates to stable housing solutions are determined in order to construct a system dynamics model of the homeless service system. This model is run for a period of 2 years, using the simulation software Vensim to identify bottlenecks as potential areas of improvement within the system. Model success is defined by HUD system performance measures, such as the length of time persons remain homeless and the rates at which persons placed in stable housing solutions return to homelessness. The model is further evaluated with the findings from a directed literature search of related case studies, semi-structured interviews with industry personnel, and a comparison to national best practices. The model will be generalized to simulate the HUD system performance measures of other homeless service systems in the United States. Additionally, the model will inform recommendations of identified improvements, such as altering ratios of case managers to facility occupants, modifying the intake assessment process, and optimizing facility programs for improved client flow. These recommendations will be applied to create a prototype dashboard for the client. This dashboard will be used as a forecasting tool to aid in decision-making affecting the operation of local homeless service systems.