Comparisons via Stochastic Simulation, with Applications to Manufacturing and Services

许多制造和服务系统是如此复杂，以至于唯一的分析工具是离散事件随机模拟。虽然模拟是一种强大的工具，但它的缺点是它是一种实验（而不是分析）分析，需要对其输出进行适当的统计分析。因为在这里，实验者对实验环境有完全的控制，许多强调独立观察的经典统计并不适用，因为实验者通常应该故意引入相关性作为减少方差的技术。这项研究涉及筛选大量系统的程序开发，以找到最佳系统，将替代系统与已知标准进行比较，并将替代系统与默认系统进行比较。有许多经典的排序和选择程序，但同样，这些假设通常的独立性假设并不总是存在于离散事件，随机模拟中。大多数经典程序也假设输出数据的正态性，并且仅用于测量总体均值。通常情况下，随机模拟的输出数据是不正常的，通常情况下，除了平均响应之外的度量是有趣的（例如，一个产品在生产中花费超过t个月的概率）。研究将考虑统计效率、待比较系统之间的功能关系、非标准性能度量、基本理论和以纳入现有流行的商业仿真软件的形式实现。随着易于使用的仿真软件的出现，通过离散事件随机系统仿真对一个相当复杂的系统进行建模变得相对容易。然而，大多数软件包没有真正足够的内置统计分析软件。本研究将完成的不仅是开发适当和有效的统计程序，而且还可以将程序纳入软件包并对用户透明，为研究复杂制造和服务系统这一非常重要的领域提供重大进展。

Nelson 9622065 A great many manufacturing and service systems are so complex that the only analysis tool is discrete-event-stochastic simulation. Although simulation is a powerful tool, it suffers from the shortcoming that it is an experimental (not analytical) analysis and requires proper statistical analysis of its output. Since here, the experimenter has complete control over the experimental environment, much of classical statistics, with its emphasis on independent observations, is not applicable, as it often behooves the experimenter to purposely introduce correlation as a variance reduction technique. This research concerns development of procedures to screen a large number of systems, to find the best system, to compare alternative systems to a known standard and to compare alternative systems to a default. There are many classical ranking and selection procedures, but again, these assume the usual independence assumptions that are not always present in discrete-event, stochastic simulation. Most of the classical procedures also assume normality of output data, and are developed to measure population means only. More often than not, output data from stochastic simulations are not normal, and often, measures other than mean responses, are of interest (e.g., the probability of an item spending more than t months in production). Statistical efficiency, functional relationships among systems to be compared, non-standard performance measures, basic theory and implementation in the form of incorporation into existing popular commercial simulation software will be considered in the research. With the advent of easy-to-use simulation software, it is relatively easy to model a rather complex system via discrete-event-stochastic system simulation. However, most packages have no really adequate statistical analysis software built-in. What this research will accomplish is not only the development of appropriate and efficient statistical procedures, but also procedures which can be inco rporated into software packages and will be transparent to the users, providing significant advancement for this very important area for studying complex manufacturing and service systems.