Correlated Decomposition for Analyzing Dynamic Stochastic Systems

分析动态随机系统的相关分解

• 批准号: 9300058
• 负责人: Michael Taaffe
• 金额: $ 42.72万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-08-15 至 1998-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9300058&HistoricalAwards=false
• 关键词: Correlated; Decomposition; Analyzing; Dynamic; Stochastic

9300058 Taaffe This research is concurrent with creating methodology and software for efficiently analyzing models of manufacturing systems, including complexities such as assembly, blocking, and time dependence. We obtain efficient analysis by combining the speed and efficiency of queueing network approximations with the details and generality of computer simulation experimentation. Our combined approach, which we refer to as Correlated-Decomposition Approximation and Simulation (CDAS), allows the strengths and weaknesses of these two methodologies to complement each other. The approximations and simulation can work together by using the approximations as control varieties or the our newly developed more general biased-control varieties, for the simulation. The goal is to improve analysis efficiency by two or three orders of magnitude compared to ordinary simulation experiments. The combined CDAS methodology should analyze the behavior of large complex manufacturing systems in a matter of seconds or minutes rather than the hours or days that simulation alone can require and to do so with accuracy beyond that possible in present queueing network approximation software.

9300058 Taaffe 本研究与方法论的创建同步进行， 用于有效分析制造系统模型的软件，包括复杂性，如装配、分块和时间 依赖 我们得到有效的分析相结合的速度和效率的网络近似的细节和计算机模拟实验的一般性。 我们 结合的方法，我们称之为相关分解近似和模拟（CDAS），允许的优势和 这两种方法的缺点是相辅相成的。 近似和模拟可以一起工作，通过使用近似作为控制品种或我们新开发的更一般的偏置控制品种，用于模拟。 的 的目标是提高分析效率的两个或三个数量级相比，普通的模拟实验。 的 综合CDAS方法应分析大型 在几秒钟或几分钟内完成复杂的制造系统 而不是模拟单独可能需要的几个小时或几天，并以超出目前模拟的可能精度来完成 网络近似软件