A Comprehensive Framework and Software for Simulation Input

该资助为开发随机模拟输入建模的综合框架提供资金，该框架可以完成以下任务：(1)代表广泛的稳态模拟输入模型，包括独立的单变量过程、有限维随机向量、平稳的单变量时间序列过程和平稳的向量-时间序列过程；(2)通过自动算法将这些输入模型与数据拟合，同时通过用户的主观判断或部分信息（如界限、百分位数或矩）对拟合模型进行直观、直接的修改；(3)快速准确地生成这些输入过程的实现，以驱动大规模的计算机模拟；(4)通过使输入模型易于根据易于理解的参数进行调整，从而便于仿真输出相对于仿真输入的灵敏度分析。该框架将基于从平稳的多变量向量时间序列中表示、拟合和生成观测值的能力，其中每个单独的分量可以具有约翰逊、贝塞尔或离散的边际分布；此外，依赖结构通过由选定的时间滞后分隔的组件对之间的积矩相关性来指定。这样的输入过程将由高斯向量自回归过程的适当变换来构造。这项研究的主要好处是，它将把可靠的输入建模从统计专家的领域中解放出来，并将其置于日常模拟用户的手中。仿真输入构成了每个随机仿真模型的核心，因此这将大大提高实际仿真模型的保真度，从而获得更准确的结果和更好的决策。由于仿真分析人员使用他们在软件中发现的东西，因此在本研究中开发并提供给商业供应商的软件应该可以加速技术转移。

This grant provides funding for the development of a comprehensive framework for stochastic simulation input modeling that can accomplish the following: (1) represent a wide range of steady-state simulation input models, including independent univariate processes, finite-dimensional random vectors, stationary univariate time-series processes, and stationary vector-time-series processes; (2) fit these input models to data via automated algorithms while enabling intuitive, direct modification of the fitted models via the user'ssubjective judgment or partial information such as bounds, percentiles, or moments; (3) generate realizations of these input processes quickly and accurately in order to drive large-scale computer simulations; and (4) facilitate sensitivity analysis of simulation outputs with respect to simulation inputs by making the input models readily adjustable in terms of easily understood parameters. The framework will be based on the ability to represent, fit, and generate observations from a stationary multivariate vector time series in which each individual component can have either a Johnson, Bezier, or discrete marginal distribution; moreover, the dependence structure is specified via product-moment correlations between pairs of components that are separated by selected time lags. Such an input process will be constructed by an appropriate transformation of a Gaussian vector autoregressive process. The primary benefit of this research is that it will take reliable input modeling out of the domain of statistical specialists and put it into the hands of everyday simulation users. Simulation inputs form the core of every stochastic simulation model, so this will substantially improve the fidelity of practical simulation models, leading to more accurate results and better decisions. Since simulation analysts use what they find in software, the software developed in this research and made available to commercial vendors should speed the technology transfer.