Collaborative Research: Effective Sequential Procedures for Risk and Error Estimation in Steady-state Simulation

该奖项为开发、实施和评估稳态仿真输出分析的综合方法提供资金，该方法在一个名为稳态仿真中风险和误差估计的有效顺序程序(ESTPRESS)的框架中进行。具体地说，将开发用于计算由概率模拟模型产生的输出过程的稳态平均值和选定的稳态分位数的点估计器和置信度区间的顺序方法。可信区间将满足预先指定的准确度标准，并在大样本中达到用户指定的可靠性水平。稳态平均值描述了长期平均表现，例如，在固定产能下运营的汽车组装厂每月生产的平均车辆数量。稳态分位数被用来衡量许多服务设施的响应性并表征与金融资产相关的风险，例如，呼叫等待时间的第90个百分位数通常用于评估呼叫中心的性能，金融资产投资组合的风险价值的第5个百分位数经常用于描述该投资组合的损失风险。ESTPRESS的实施阶段将产生计算效率高的统计估计程序，用户可以方便快捷地获取和应用，这些程序将在评价阶段进行测试。这项工作的最终目标是为从业者和研究人员提供新的技术和公共领域软件，用于分析完全自动化的、健壮的、可靠的、计算和统计效率高的稳态仿真。如果成功，这项研究将导致全自动顺序稳态均值和分位数估计程序，这是几乎所有广泛使用的商业仿真程序所缺乏的。ESTPRESS将直接适用于政府和军事部门以及各种行业的大规模模拟研究，包括航空航天、分销、金融、医疗保健、制造、电信和交通运输。教育外展将是这个项目的一个关键组成部分，因为所有三名研究人员都对本科生和研究生的工程教育有着深切的承诺。该项目的成果将用于为“思维定势项目”(www.Mindsetproject t.org)编写教学模块，并用于在两个参与机构的硕士和博士级别开发关于蒙特卡罗方法及其应用的新课程。

This award provides funding for the development, implementation, and evaluation of a comprehensive methodology for steady-state simulation output analysis in a framework called Effective Sequential Procedures for Risk and Error Estimation in Steady-state Simulation (ESPRESS). Specifically, sequential methods for computing point estimators and confidence intervals for the steady-state mean and selected steady-state quantiles of an output process generated by a probabilistic simulation model will be developed. The confidence intervals will meet prespecified accuracy criteria and achieve a user-specified level of reliability in large samples. The steady-state mean describes long-run average performance, e.g., the average number of vehicles produced per month by an automotive assembly plant operating under a fixed capacity. Steady-state quantiles are used to measure the responsiveness of many service facilities and to characterize the risk associated with financial assets,e.g., the 90th percentile of call-waiting time is often used to evaluate the performance of a call center and the 5th percentile of the Value at Risk for a portfolio of financial assets is frequently used to describe the risk of loss on that portfolio. The implementation phase of ESPRESS will lead to computationally efficient statistical-estimation procedures that users can access and apply easily and quickly, which will be tested in the evaluation phase. The ultimate objective of this work is to provide practitioners and researchers with new techniques and public-domain software for the analysis of steady-state simulations that are completely automated, robust, and reliable as well as computationally and statistically efficient.If successful, the research will lead to fully automated sequential steady-state mean- and quantile-estimation procedures which are lacking in virtually all widely used commercial simulation packages. ESPRESS will be directly applicable to large-scale simulation studies in the governmental and military sectors as well as in a variety of industries, including aerospace, distribution, finance, healthcare, manufacturing, telecommunications, and transportation. Educational outreach will be a key component of this project, because all three investigators share a deep commitment to engineering education at both the undergraduate and graduate levels. Results of this project will be used in developing instructional modules for Project MINDSET (www.mindsetproject.org) and for new courses on Monte Carlo methods and their applications that will be developed at both the master's and doctoral levels at the two participating institutions.