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Scheduling and Routing Under Uncertainty With Predictions
Scheduling and Routing Under Uncertainty With Predictions
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152725
- ISBN
- 9798384011576
- DDC
- 621.3
- 저자명
- Wei, Hao-Ting.
- 서명/저자
- Scheduling and Routing Under Uncertainty With Predictions
- 발행사항
- [Sl] : Columbia University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 193 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
- 주기사항
- Advisor: Stein, Clifford;Balkanski, Eric.
- 학위논문주기
- Thesis (Ph.D.)--Columbia University, 2024.
- 초록/해제
- 요약Uncertainty surrounds us daily, indicating the need for effective decision-making strategies. In recent years, the large amount of available data has accelerated the development of novel methods for decision-making and optimization. This thesis studies this inquiry, centering on a framework that employs predictions to enhance decision-making in various optimization problems.We investigate scheduling and routing problems, which are fundamental in the field of sequential decision-making and optimization, within the framework of algorithms with predictions. Our goal is to improve performance by integrating predictions of unknown input parameters. The central question is: "Can we design algorithms that use predictions to enhance performance when the prediction is accurate while still maintaining worst-case guarantees, even when the predictions are inaccurate?"Through theoretical and experimental analyses, we demonstrate that by incorporating appropriate predictions of unknown input parameters, we design algorithms to outperform existing results when predictions are accurate while maintaining worst-case guarantees even when the predictions are significantly erroneous.
- 일반주제명
- Computer engineering
- 일반주제명
- Computer science
- 키워드
- Algorithms
- 키워드
- Routing
- 키워드
- Scheduling
- 키워드
- Prediction
- 기타저자
- Columbia University Operations Research
- 기본자료저록
- Dissertations Abstracts International. 86-02B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211152725
■006m o d
■007cr#unu||||||||
■020 ▼a9798384011576
■035 ▼a(MiAaPQ)AAI31490090
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a621.3
■1001 ▼aWei, Hao-Ting.
■24510▼aScheduling and Routing Under Uncertainty With Predictions
■260 ▼a[Sl]▼bColumbia University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a193 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-02, Section: B.
■500 ▼aAdvisor: Stein, Clifford;Balkanski, Eric.
■5021 ▼aThesis (Ph.D.)--Columbia University, 2024.
■520 ▼aUncertainty surrounds us daily, indicating the need for effective decision-making strategies. In recent years, the large amount of available data has accelerated the development of novel methods for decision-making and optimization. This thesis studies this inquiry, centering on a framework that employs predictions to enhance decision-making in various optimization problems.We investigate scheduling and routing problems, which are fundamental in the field of sequential decision-making and optimization, within the framework of algorithms with predictions. Our goal is to improve performance by integrating predictions of unknown input parameters. The central question is: "Can we design algorithms that use predictions to enhance performance when the prediction is accurate while still maintaining worst-case guarantees, even when the predictions are inaccurate?"Through theoretical and experimental analyses, we demonstrate that by incorporating appropriate predictions of unknown input parameters, we design algorithms to outperform existing results when predictions are accurate while maintaining worst-case guarantees even when the predictions are significantly erroneous.
■590 ▼aSchool code: 0054.
■650 4▼aComputer engineering
■650 4▼aComputer science
■653 ▼aAlgorithms
■653 ▼aOnline algorithms
■653 ▼aRouting
■653 ▼aScheduling
■653 ▼aPrediction
■690 ▼a0796
■690 ▼a0984
■690 ▼a0464
■71020▼aColumbia University▼bOperations Research.
■7730 ▼tDissertations Abstracts International▼g86-02B.
■790 ▼a0054
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17163567▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
