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

协作研究：稳态仿真中风险和误差估计的有效顺序程序

• 批准号: 1232998
• 负责人: James Wilson
• 金额: $ 20.96万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1232998&HistoricalAwards=false
• 关键词: Collaborative; Research; Effective; Sequential; Procedures

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.

该奖项为稳态仿真输出分析的综合方法的开发，实施和评估提供资金，该方法在称为稳态仿真风险和误差估计的有效顺序程序（ESPRESS）的框架中进行。具体而言，连续的方法计算点估计值和置信区间的稳态平均值和选定的稳态分位数的输出过程中产生的概率模拟模型将开发。置信区间将符合预先规定的准确度标准，并在大样本中达到用户规定的可靠性水平。稳态平均值描述长期平均性能，例如，汽车装配厂在固定产能下每月平均生产的汽车数量。稳态分位数用于衡量许多服务设施的响应能力，并描述与金融资产相关的风险，例如，呼叫等待时间的第90百分位数经常用于评估呼叫中心的性能，而金融资产组合的风险价值的第5百分位数经常用于描述该组合的损失风险。ESPRESS的实施阶段将产生计算效率高的估算程序，用户可以方便快捷地获取和应用这些程序，并将在评价阶段对其进行测试。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的软件，用于分析完全自动化，鲁棒性和可靠性以及计算和统计效率的稳态模拟。如果成功，这项研究将导致全自动连续稳态均值和分位数，这是几乎所有广泛使用的商业模拟软件包所缺乏的估计程序。ESPRESS将直接适用于政府和军事部门以及各种行业的大规模仿真研究，包括航空航天，分销，金融，医疗保健，制造，电信和运输。教育推广将是这个项目的一个关键组成部分，因为所有三个研究人员都对本科和研究生阶段的工程教育有着深刻的承诺。该项目的成果将用于为“思维”项目（www.mindsetproject.org）和为两个参与机构的硕士和博士生开设的关于蒙特卡罗方法及其应用的新课程开发教学模块。