Efficient Simulation of Large-Scale Systems

大型系统的高效仿真

• 批准号: 9900117
• 负责人: James Calvin
• 金额: $ 18.94万
• 依托单位: New Jersey Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-06-15 至 2004-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9900117&HistoricalAwards=false
• 关键词: Efficient; Simulation; Large; Scale; Systems

This grant provides funding for the development of new methodologies for improving the efficiency of simulations of large-scale stochastic systems, such as those arising in manufacturing, telecommunications, production/inventory, finance, and transportation. The basic ideas underlying many of the award techniques is to extract or utilize more information from a simulation than is done in the standard approaches.The methods are related to existing variance-reduction techniques such as control variates, which collect additional data that are used to modify the estimator, leading to less variability. The Principal Investigators propose to develop other approaches that similarly exploit ignored information from simulations to improve the estimator. Several of the proposed techniques take a particular realization of the simulation output and construct many other possible realizations from it. Estimates of the performance measure of interest are computed from each realization, and the estimates are then averaged. This leads to lower variability thanthe standard approach. The Principal Investigators plan to establish the validity of these methodologies under various assumptions and develop computationally efficient implementations of the ideas. One of the desired outcomes is to show that the proposed methods are optimal under certain conditions. If successful, the results of the project will lead to significant improvements in the efficiency of simulations of large-scale systems. The basic ideas underlying the methods are quite general and versatile, and they can be combined with other existing simulation techniques.

该赠款为开发新方法提供资金，以提高大规模随机系统的模拟效率，例如制造业，电信，生产/库存，金融和运输中出现的系统。 许多奖励技术的基本思想是从模拟中提取或利用比标准方法更多的信息，这些方法与现有的方差减少技术有关，例如控制变量，它收集用于修改估计量的额外数据，从而减少可变性。 主要研究人员建议开发其他方法，类似地利用模拟中被忽略的信息来改进估计器。 所提出的技术中有几种采用模拟输出的特定实现，并从中构建许多其他可能的实现。从每个实现计算感兴趣的性能度量的估计，然后对估计进行平均。 这导致比标准方法更低的可变性。 主要研究人员计划在各种假设下建立这些方法的有效性，并开发这些想法的计算效率实现。 期望的结果之一是表明所提出的方法在某些条件下是最优的。 如果成功，该项目的结果将导致大规模系统模拟效率的显著提高。这些方法的基本思想是通用的，并且可以与其他现有的仿真技术相结合。