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.

该教师早期职业发展计划 (CAREER) 拨款通过为数据驱动模拟创建强大的决策框架来促进国民健康、繁荣和福利。由于模拟在捕获系统随机性方面具有灵活性，因此已成为支持决策的流行工具。然而，仿真分析存在由于实际系统与仿真模型之间存在差异而得出错误统计推论的“模型风险”。考虑到此类风险可能会导致低质量的决策本研究的重点是根据现有数据估计模拟模型中驱动随机性的概率分布函数时出现的“输入模型风险”。特别是，将研究一种新的稳健决策框架，以平衡实际用户输入对模拟模型中统计误差的可接受的次优性和稳健性。和下一代 STEM 劳动力，使模型风险成为这项研究将使复杂模拟系统的输入模型风险量化成为可能，而迄今为止由于计算复杂性而实际上是不可行的。此外，通过应用似然比方法并解决双层优化问题，将创建一个高斯过程（GP）元模型来预测作为参数和非参数输入模型的函数的模拟输出平均值。设计鲁棒模拟分析生命周期所有三个步骤的综合框架：(1) 风险量化，(2) 鲁棒优化，(3) 风险降低 “实际上鲁棒”最优性的概念将通过考虑来重新定义。该框架将减少现有方法的保守性，同时实现用户期望的鲁棒性水平，为了找到实际鲁棒的最佳值，将采用由 GP 推理引导的顺序分配模拟工作的优化算法。被创建。最后，通过优化数据收集计划，为降低输入模型风险提供可操作的指导，以获得更强大的统计性能保证，实现实际稳健的最佳结果。该奖项反映了 NSF 的法定使命，并通过使用基金会的评估进行评估，认为值得支持。智力价值和更广泛的影响审查标准。