A Comprehensive Framework and Software for Simulation Input Modeling

这笔赠款为开发随机模拟输入建模的综合框架提供了资金，该框架可以完成以下工作：(1)表示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矢量-时间序列过程；(2)通过自动算法将这些输入模型与数据进行拟合，同时能够通过用户的主观判断或部分信息(如界、百分位数或矩)直观、直接地修改拟合的模型；(3)快速而准确地生成这些输入过程的实现，以便驱动大规模的计算机模拟；以及(4)通过使输入模型容易地根据易于理解的参数进行调整，便于模拟输出相对于模拟输入的灵敏度分析。该框架将基于从平稳的多变量向量时间序列表示、拟合和生成观测的能力，其中每个单独的分量可以具有Johnson、Bezier或离散的边际分布；此外，相关性结构通过由选定的时间滞后分开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建。这项研究的主要好处是，它将把可靠的输入建模带出统计专家的领域，并将其交到日常模拟用户手中。模拟输入构成了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逼真度，导致更准确的结果和更好的决策。由于模拟分析人员使用他们在软件中发现的东西，因此在这项研究中开发并向商业供应商提供的软件应该会加快技术转让。

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 vendorsshould speed the technology transfer.