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Demand Projection and Complex Resource Allocation Decisions on Networks
Demand Projection and Complex Resource Allocation Decisions on Networks
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260209102907
- ISBN
- 9798263397777
- DDC
- 519.77
- 서명/저자
- Demand Projection and Complex Resource Allocation Decisions on Networks
- 발행사항
- [Sl] : Georgia Institute of Technology, 2023
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2023
- 형태사항
- 186 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-05, Section: B.
- 주기사항
- Advisor: Keskinocak, Pinar;Singh, Mohit.
- 학위논문주기
- Thesis (Ph.D.)--Georgia Institute of Technology, 2023.
- 초록/해제
- 요약In this thesis, we consider various practical and theoretical problems arising from health and humanitarian systems that involve the projection of demand and the allocation of resources to populations, which are represented in a network structure. These problems include: (i) quantifying the impact of non-pharmaceutical interventions (NPIs) on disease spread and the need for hospital capacity, (ii) assessing the trade-offs between the public health benefits, such as the reduction in infection spread and adverse outcomes, and other consequences of NPIs, particularly in terms of the number of homebound individuals or person-days, (iii) partitioning a disaster-impacted area among multiple heterogeneous resources to obtain connected components of similar weight, where the weights are nodeand resource-specific. To address these problems effectively, we employ mathematical modeling approaches, in particular, agent-based simulation models and optimization-based techniques tailored for each problem, allowing us to capture the network component in our analyses.Chapter 1 provides a brief introduction and a background to the research problems addressed in this thesis. In Chapter 2, we evaluate the effectiveness and impact of nonpharmaceutical intervention decisions, including school closures, shelter-in-place orders, and voluntary quarantine, using an agent-based simulation model focused on the state of Georgia. We test various scenarios with different durations of shelter-in-place measures and time-varying compliance levels to voluntary quarantine. The outcomes of these simulations provide valuable insights to decision-makers, enabling them to make informed recommendations for social distancing measures to the public. Chapter 3 quantifies the impact of non-pharmaceutical interventions and investigates the trade-offs between their potential benefits, such as reduction in infection spread and adverse outcomes, and socioeconomic consequences of refraining from workplace and community interactions. We measure these trade-offs by considering the number of homebound individuals or person-days and the number of infections and deaths. These evaluations can assist local and national decision-makers in choosing different combinations of interventions over time to reduce infection spread while considering the societal and economic impact.In Chapter 4, we focus on the problem of partitioning a graph among multiple heterogeneous resources to obtain connected components of similar weight, where the weights are node- and resource-specific. We formulate this problem as novel integer and mixed-integer programs, develop approximation algorithms with provable bounds for special graph and weight structures, and propose several heuristics. We also present an extensive computational study, including a realistic hurricane scenario for the state of Florida, focusing on the post-disaster debris collection application of this problem, where the objective is to move debris from the disaster-affected area efficiently. Chapter 5 focuses on special cases of the problem by considering the connectivity and planarity of the underlying graph, as it significantly affects the problem's computational complexity. We analyze the complexity of various special cases and present approximation algorithms for planar and non-planar graphs with certain connectivity. Building on the theoretical foundations, we introduce heuristics for general graphs. We present the results of a computational study focusing on a hurricane scenario for the state of Florida.
- 일반주제명
- Integer programming
- 일반주제명
- Computer science
- 기본자료저록
- Dissertations Abstracts International. 87-05B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008260203s2023 us c eng d■001000017365982
■00520260209102907
■006m o d
■007cr#unu||||||||
■020 ▼a9798263397777
■035 ▼a(MiAaPQ)AAI32315761
■035 ▼a(MiAaPQ)GeorgiaTech76748
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a519.77
■1001 ▼aAglar, Buse Eylul Oruc.
■24510▼aDemand Projection and Complex Resource Allocation Decisions on Networks
■260 ▼a[Sl]▼bGeorgia Institute of Technology▼c2023
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2023
■300 ▼a186 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-05, Section: B.
■500 ▼aAdvisor: Keskinocak, Pinar;Singh, Mohit.
■5021 ▼aThesis (Ph.D.)--Georgia Institute of Technology, 2023.
■520 ▼aIn this thesis, we consider various practical and theoretical problems arising from health and humanitarian systems that involve the projection of demand and the allocation of resources to populations, which are represented in a network structure. These problems include: (i) quantifying the impact of non-pharmaceutical interventions (NPIs) on disease spread and the need for hospital capacity, (ii) assessing the trade-offs between the public health benefits, such as the reduction in infection spread and adverse outcomes, and other consequences of NPIs, particularly in terms of the number of homebound individuals or person-days, (iii) partitioning a disaster-impacted area among multiple heterogeneous resources to obtain connected components of similar weight, where the weights are nodeand resource-specific. To address these problems effectively, we employ mathematical modeling approaches, in particular, agent-based simulation models and optimization-based techniques tailored for each problem, allowing us to capture the network component in our analyses.Chapter 1 provides a brief introduction and a background to the research problems addressed in this thesis. In Chapter 2, we evaluate the effectiveness and impact of nonpharmaceutical intervention decisions, including school closures, shelter-in-place orders, and voluntary quarantine, using an agent-based simulation model focused on the state of Georgia. We test various scenarios with different durations of shelter-in-place measures and time-varying compliance levels to voluntary quarantine. The outcomes of these simulations provide valuable insights to decision-makers, enabling them to make informed recommendations for social distancing measures to the public. Chapter 3 quantifies the impact of non-pharmaceutical interventions and investigates the trade-offs between their potential benefits, such as reduction in infection spread and adverse outcomes, and socioeconomic consequences of refraining from workplace and community interactions. We measure these trade-offs by considering the number of homebound individuals or person-days and the number of infections and deaths. These evaluations can assist local and national decision-makers in choosing different combinations of interventions over time to reduce infection spread while considering the societal and economic impact.In Chapter 4, we focus on the problem of partitioning a graph among multiple heterogeneous resources to obtain connected components of similar weight, where the weights are node- and resource-specific. We formulate this problem as novel integer and mixed-integer programs, develop approximation algorithms with provable bounds for special graph and weight structures, and propose several heuristics. We also present an extensive computational study, including a realistic hurricane scenario for the state of Florida, focusing on the post-disaster debris collection application of this problem, where the objective is to move debris from the disaster-affected area efficiently. Chapter 5 focuses on special cases of the problem by considering the connectivity and planarity of the underlying graph, as it significantly affects the problem's computational complexity. We analyze the complexity of various special cases and present approximation algorithms for planar and non-planar graphs with certain connectivity. Building on the theoretical foundations, we introduce heuristics for general graphs. We present the results of a computational study focusing on a hurricane scenario for the state of Florida.
■590 ▼aSchool code: 0078.
■650 4▼aInteger programming
■650 4▼aComputer science
■690 ▼a0984
■71020▼aGeorgia Institute of Technology.
■7730 ▼tDissertations Abstracts International▼g87-05B.
■790 ▼a0078
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17365982▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
