Comparisons via Stochastic Simulation, with Applications to Manufacturing and Services

通过随机模拟进行比较以及在制造和服务业中的应用

• 批准号: 9622065
• 负责人: Barry Nelson
• 金额: $ 21.9万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-06-01 至 2000-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9622065&HistoricalAwards=false
• 关键词: Comparisons; via; Stochastic; Simulation; Applications

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.

纳尔逊9622065 大量的制造和服务系统是如此复杂，唯一的分析工具是离散事件随机仿真。 虽然模拟是一个强大的工具，但它的缺点是它是一个实验（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，实验者完全控制了实验环境，许多强调独立观察的经典统计学是不适用的，因为实验者经常有必要故意引入相关性作为方差减少技术。 这项研究涉及的程序开发筛选大量的系统，找到最好的系统，比较替代系统的一个已知的标准和比较替代系统的默认值。 有许多经典的排名和选择程序，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。 大多数经典的程序也假设输出数据的正态性，并开发来衡量人口的手段。 通常情况下，随机模拟的输出数据是不正常的，并且通常，除了平均响应之外的测量是感兴趣的（例如，项目在生产中花费超过t个月的概率）。统计效率，系统之间的功能关系进行比较，非标准的性能指标，基本理论和实施的形式纳入现有的流行的商业仿真软件将在研究中考虑。 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相当复杂的系统建模相对容易。 然而，大多数软件包都没有内置足够的统计分析软件。 这项研究将完成的不仅是适当的和有效的统计程序的发展，而且程序，可以incorrporated到软件包，将是透明的用户，提供了这个非常重要的领域研究复杂的制造和服务系统的显着进步。