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

在复杂和随机系统中，仿真和优化技术经常被用来评估系统性能和辅助决策。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设计稳健和风险感知策略。由于所提议的方法具有普遍性，由此产生的技术将在广泛的工业和科学部门具有广泛的适用性。通过与业界研究人员的合作，开发的算法将在共享经济应用领域的问题上进行测试和应用。该项目还将通过招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的集合，输入的不确定性给决策带来了巨大的风险。这个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健壮性的决策系统，并处理按时间顺序到达的流数据。该项目包括三个主要研究方向：a)在线量化输入模型不确定性，并保证参数和非参数输入模型的收敛；b)在新的贝叶斯风险优化框架下，在输入模型不确定性下的模拟优化，其目的是平衡优化预期性能和对冲与输入模型风险；c)输入模型不确定性下的排序和选择，将开发新的算法，并给出收敛速度结果；这些奖项将考虑通过收集更多数据来减少输入不确定性和通过运行更多模拟实验来减少模拟不确定性之间的权衡。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.