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.

该奖项为在稳态模拟（ESPRESS）中称为有效的顺序估算的框架中，为稳态模拟输出分析的综合方法提供了用于稳态模拟输出分析的综合方法的资金。具体而言，将开发用于计算点估计器和置信区间的顺序方法，用于稳态均值和选定的稳态分位数，该方法将开发由概率模拟模型生成的输出过程的稳态分位数。置信区间将符合预先指定的准确性标准，并在大型样本中实现用户指定的可靠性水平。稳态平均值描述了长期平均性能，例如，在固定容量下运行的汽车装配厂每月生产的车辆平均数量。稳态分位数用于衡量许多服务设施的响应能力，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绩效，以及在金融资产投资组合的风险中的5个百分位数，以描述该资产损失的风险。浓缩咖啡的实施阶段将导致用户可以轻松，快速访问并应用的计算高效统计估计程序，这将在评估阶段进行测试。这项工作的最终目标是为从业者和研究人员提供新技术和公共域软件，以分析完全自动化，健壮和可靠的稳态模拟以及计算和统计上有效的效率。浓缩咖啡将直接适用于政府和军事部门以及包括航空航天，分销，金融，医疗保健，制造业，电信和运输等各种行业的大型模拟研究。教育外展将是该项目的关键组成部分，因为所有三位调查人员都对本科和研究生级别的工程教育都有深厚的承诺。该项目的结果将用于开发项目思维定势的教学模块（www.mindsetproject.org），以及有关蒙特卡洛方法的新课程及其在两个参与机构的硕士和博士水平上都将开发的应用程序。