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Addressing Input Model Uncertainty in Stochastic Simulation: From Quantification to Optimization

解决随机仿真中的输入模型不确定性：从量化到优化

基本信息

批准号：
2053489
负责人：
Enlu Zhou
金额：
$ 9.97万
依托单位：
Georgia Tech Research Corporation
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-01 至 2024-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2053489&HistoricalAwards=false
关键词：
Addressing Input Model Uncertainty Stochastic

项目摘要

Simulation and optimization techniques are often used to evaluate system performance and facilitate decision making in complex and stochastic systems. This project aims to quantify the risk associated with simulation modeling and analysis, and to design robust and risk-aware strategies for decision making based on simulation. Because of the generality of the proposed approaches, the resulting techniques will have broad applicability in a wide array of industry and science sectors. Through collaborations with researchers in industry, the developed algorithms will be tested on and applied to problems in the application area of sharing economy. This project will also provide training opportunities for underrepresented groups through recruiting and outreach activities. Stochastic simulation is often used for performance analysis and decision making in complex systems. The input to the simulations is a collection of distributions based on data, and uncertainty in the input brings significant risk to decision making. The goal of this project is to develop theory and methods that quantify the risk associated with input uncertainty, support decision making systems that are robust to the risk associated with the input uncertainty, and handle streaming data which arrive sequentially in time. The project consists of three major research thrusts including a) online quantification of input model uncertainty developed with convergence guarantees for both parametric and non-parametric input models, b) simulation optimization under input model uncertainty within a new framework of Bayesian risk optimization which aims to balance optimizing the expected performance and hedging against the input model risk, and c) ranking and selection under input model uncertainty for which new algorithms will be developed with convergence rate results; these will take into account the trade-off between reducing the input uncertainty via collecting more data and reducing the simulation uncertainty by running more simulation experiments.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

仿真和优化技术通常用于评估系统性能，并促进复杂和随机系统的决策。该项目旨在量化与仿真建模和分析相关的风险，并为基于仿真的决策制定设计鲁棒性和风险意识策略。由于所提出的方法的一般性，所产生的技术将在广泛的工业和科学领域具有广泛的适用性。通过与行业研究人员的合作，开发的算法将被测试并应用于共享经济应用领域的问题。该项目还将通过征聘和外联活动为任职人数不足的群体提供培训机会。随机模拟常用于复杂系统的性能分析和决策。模拟的输入是基于数据的分布集合，输入的不确定性给决策带来了重大风险。该项目的目标是开发量化与输入不确定性相关的风险的理论和方法，支持对与输入不确定性相关的风险具有鲁棒性的决策系统，并处理按时间顺序到达的流数据。该项目包括三个主要的研究方向，包括a）在线量化输入模型的不确定性，为参数和非参数输入模型提供收敛保证，B）在贝叶斯风险优化的新框架内，在输入模型不确定性下进行仿真优化，旨在平衡优化预期性能和对冲输入模型风险，以及c）在输入模型不确定性下的排序和选择，对于该输入模型不确定性，将开发具有收敛速度结果的新算法;这些建议会考虑到业界的情况：通过收集更多的数据来减少输入不确定性和通过运行更多的模拟实验来减少模拟不确定性之间的差距。基金会的使命是履行其使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评价，被认为值得支持。

项目成果

期刊论文数量（11）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Risk-averse Contextual Multi-armed Bandit Problem with Linear Payoffs

具有线性收益的风险规避上下文多臂老虎机问题

DOI：
发表时间：
2022
期刊：
Journal of systems science and systems engineering
影响因子：
1.2
作者：
Yifan Lin;Yuhao Wang;Enlu Zhou
通讯作者：
Enlu Zhou

Fixed Budget Ranking and Selection with Streaming Input Data

DOI：
10.1109/wsc57314.2022.10015327
发表时间：
2022-12
期刊：
2022 Winter Simulation Conference (WSC)
影响因子：
0
作者：
Yuhao Wang;Enlu Zhou
通讯作者：
Yuhao Wang;Enlu Zhou

Risk-Aware Model Predictive Control Enabled by Bayesian Learning

贝叶斯学习支持的风险感知模型预测控制

DOI：
10.23919/acc53348.2022.9867207
发表时间：
2022
期刊：
Proceedings of 2022 American Control Conference
影响因子：
0
作者：
Li, Yingke;Lin, Yifan;Zhou, Enlu;Zhang, Fumin
通讯作者：
Zhang, Fumin

Noise Regularizes Over-parameterized Rank One Matrix Recovery, Provably

DOI：
发表时间：
2022-02
期刊：
影响因子：
0
作者：
Tianyi Liu;Yan Li;Enlu Zhou;Tuo Zhao
通讯作者：
Tianyi Liu;Yan Li;Enlu Zhou;Tuo Zhao

Robust Multi-Objective Bayesian Optimization Under Input Noise

DOI：
发表时间：
2022-02
期刊：
影响因子：
0
作者：
Sam Daulton;Sait Cakmak;M. Balandat;Michael A. Osborne;Enlu Zhou;E. Bakshy
通讯作者：
Sam Daulton;Sait Cakmak;M. Balandat;Michael A. Osborne;Enlu Zhou;E. Bakshy