A Comprehensive Framework and Software for Simulation Input

该基金为随机模拟输入建模的综合框架的开发提供资金，该框架可以实现以下目标：（1）表示广泛的稳态模拟输入模型，包括独立单变量过程，有限维随机向量，平稳单变量时间序列过程和平稳向量时间序列过程;（2）通过自动算法将这些输入模型拟合到数据，同时通过用户的主观判断或部分信息（例如边界、曲线或矩）来实现对拟合模型的直观、直接修改;（3）快速准确地生成这些输入过程的实现，以驱动大规模计算机模拟;以及（4）通过使输入模型在容易调整方面容易调整，了解参数 该框架将基于从固定的多变量向量时间序列中表示、拟合和生成观测结果的能力，其中每个单独的分量可以具有约翰逊、贝塞尔或离散边缘分布;此外，依赖结构通过由选定的时间滞后分离的成对分量之间的乘积矩相关性来指定。 这样的输入过程将通过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，并将其交给日常模拟用户。 模拟输入是每个随机模拟模型的核心，因此这将大大提高实际模拟模型的保真度，从而获得更准确的结果和更好的决策。 由于仿真分析师使用他们在软件中找到的东西，因此在本研究中开发并提供给商业供应商的软件应该会加速技术转让。

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.