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Data Driven Simulation Frameworks for Operational Decision Making
Data Driven Simulation Frameworks for Operational Decision Making
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103105
- ISBN
- 9798288863073
- DDC
- 658
- 저자명
- Aykanat, Dilara.
- 서명/저자
- Data Driven Simulation Frameworks for Operational Decision Making
- 발행사항
- [Sl] : University of California, Berkeley, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 97 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-01, Section: B.
- 주기사항
- Advisor: Zheng, Zeyu.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Berkeley, 2025.
- 초록/해제
- 요약Many real world processes can be modeled as stochastic systems that necessitate simulations to evaluate multiple scenarios. However, uncertainties in input data, limited historical information, high computational costs and the need for extensive simulations create several challenges: i) The exact optimization of the final objective might be infeasible or costly which makes the simulation necessary. ii) The past data may not be sufficient to estimate the input parameters with high confidence. iii) Errors in the input distribution estimates can propagate and potentially affect the final objective predictions which may lead to suboptimal decisions. This dissertation presents simple and versatile simulation frameworks for data-driven operational decision making: i) We introduce a regularized simulation setting where the task's objective is considered in the training of the generators for a more accurate scenario planning. ii) We propose a simple general approach to the classical newsvendor problem with limited past information that balances the uncertainty risk. Our solutions can be adapted to any type of generator, predictor and do not rely on specific assumptions regarding the final objective function. Finally, we introduce a Taboo game benchmark for large language models to evaluate their ability to express words under constraints, which has potential applications in AI safety, training and content moderation.
- 일반주제명
- Industrial engineering
- 일반주제명
- Computer science
- 기타저자
- University of California, Berkeley Industrial Engineering & Operations Research
- 기본자료저록
- Dissertations Abstracts International. 87-01B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798288863073
■035 ▼a(MiAaPQ)AAI31935383
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a658
■1001 ▼aAykanat, Dilara.
■24510▼aData Driven Simulation Frameworks for Operational Decision Making
■260 ▼a[Sl]▼bUniversity of California, Berkeley▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a97 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-01, Section: B.
■500 ▼aAdvisor: Zheng, Zeyu.
■5021 ▼aThesis (Ph.D.)--University of California, Berkeley, 2025.
■520 ▼aMany real world processes can be modeled as stochastic systems that necessitate simulations to evaluate multiple scenarios. However, uncertainties in input data, limited historical information, high computational costs and the need for extensive simulations create several challenges: i) The exact optimization of the final objective might be infeasible or costly which makes the simulation necessary. ii) The past data may not be sufficient to estimate the input parameters with high confidence. iii) Errors in the input distribution estimates can propagate and potentially affect the final objective predictions which may lead to suboptimal decisions. This dissertation presents simple and versatile simulation frameworks for data-driven operational decision making: i) We introduce a regularized simulation setting where the task's objective is considered in the training of the generators for a more accurate scenario planning. ii) We propose a simple general approach to the classical newsvendor problem with limited past information that balances the uncertainty risk. Our solutions can be adapted to any type of generator, predictor and do not rely on specific assumptions regarding the final objective function. Finally, we introduce a Taboo game benchmark for large language models to evaluate their ability to express words under constraints, which has potential applications in AI safety, training and content moderation.
■590 ▼aSchool code: 0028.
■650 4▼aIndustrial engineering
■650 4▼aComputer science
■653 ▼aLarge language models
■653 ▼aNewsvendor problem
■653 ▼aContent moderation
■690 ▼a0546
■690 ▼a0984
■690 ▼a0800
■71020▼aUniversity of California, Berkeley▼bIndustrial Engineering & Operations Research.
■7730 ▼tDissertations Abstracts International▼g87-01B.
■790 ▼a0028
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17356943▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
