CAREER: Advancing Theory and Practice of Robust Simulation Analysis Under Input Model Risk

职业：推进输入模型风险下稳健仿真分析的理论和实践

基本信息

批准号：
2045400
负责人：
Eunhye Song
金额：
$ 50.76万
依托单位：
Pennsylvania State Univ University Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2022-10-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2045400&HistoricalAwards=false
关键词：
CAREER Advancing Theory Practice Robust

项目摘要

This Faculty Early Career Development Program (CAREER) grant advances the national health, prosperity and welfare by creating a robust decision-making framework for data-driven simulation. Due to its flexibility in capturing system randomness, simulation has been a popular tool to support decision-making problems that arise in manufacturing, healthcare, defense, finance, and other domains. However, simulation analysis is subject to “model risk” of drawing an incorrect statistical inference due to discrepancy between the real system and the simulation model. Failure to account for such risk may lead to poor quality decisions made on the basis of these models. This research focuses on “input model risk” that arises when the probability distribution functions driving randomness in a simulation model are estimated based on the available data. The project will study methods to quantify, reduce, and ensure robust decisions under input model risk. In particular, a new robust decision-making framework will be studied to balance a practical user input on acceptable suboptimality and robustness to the statistical error in the simulation model. The education mission of this grant is to train current and next-generation STEM workforce to make model risk a central focus of simulation analysis and equip them with computational tools to employ. This research will enable input model risk quantification for complex simulated systems that are here-to-fore practically infeasible due to computationally complexity. A minimum-cost simulation experiment design will be obtained by applying the likelihood ratio method and solving a bilevel optimization problem. Moreover, a Gaussian process (GP) metamodel will be created to predict the simulation output mean as a function of both parametric and nonparametric input models. This GP metamodel will serve as a vehicle to design a comprehensive framework for all three steps of the robust simulation analysis life cycle: (1) risk quantification, (2) robust optimization, and (3) risk reduction. The concept of “practically robust” optimality will be newly defined by accounting for the user-specified practical optimality gap of interest. This framework will reduce conservatism of existing methods while achieving the level of robustness the user desires. To find a practically robust optimum, an efficient simulation optimization algorithm, which sequentially allocates simulation effort guided by GP inference, will be created. Finally, an actionable guidance to reduce input model risk will be provided by optimizing the data collection plan to attain a stronger statistical performance guarantee for the practically robust optimum.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.

这项教师的早期职业发展计划（职业）赠款通过为数据驱动的模拟创建强大的决策框架来提高国家健康，繁荣和福利。由于其在捕获系统随机性方面的灵活性，模拟一直是支持制造，医疗保健，国防，金融和其他领域中出现的决策问题的流行工具。但是，仿真分析要受到由于实际系统和仿真模型之间差异而绘制错误的统计推断的“模型风险”。不考虑这种风险可能会导致基于这些模型做出质量差的决策。这项研究重点关注“输入模型风险”，这些风险是根据可用数据估算模拟模型中驱动随机性的概率分布函数时会产生的。该项目将研究量化，减少和确保在输入模型风险下的强大决策的方法。特别是，将研究一个新的强大决策框架，以在模拟模型中对可接受的次级临时性和鲁棒性的实用用户输入平衡。这项赠款的教育使命是培训当前和下一代STEM劳动力，以使模型风险成为模拟分析的主要重点，并为其配备使用计算工具。这项研究将使由于计算上的复杂性而导致的复杂模拟系统实现输入模型的风险数量。通过应用似然比方法并解决双重优化问题，将获得最低成本的模拟实验设计。此外，将创建一个高斯过程（GP）元模型，以预测仿真输出均值作为参数和非参数输入模型的函数。该GP Metamodel将作为设计综合框架的工具，用于强大的仿真分析生命周期的所有三个步骤：（1）风险数量，（2）强大的优化和（3）降低风险。 “实际上强大”最优性的概念将通过考虑用户指定的实践最优差距的新定义。该框架将减少现有方法的保守主义，同时达到用户期望的鲁棒性水平。为了找到实际上可靠的最优值，将创建一种有效的仿真优化算法，该算法将创建由GP推断引导的模拟工作。最后，通过优化数据收集计划以实现实际强大的最佳最佳统计绩效保证，可以为减少输入模型风险提供可行的指南。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的审查标准通过评估来评估的支持。