Multiple Comparisons for Optimization via Simulation

通过模拟进行优化的多重比较

• 批准号: 8922721
• 负责人: Barry Nelson
• 金额: $ 10.23万
• 依托单位: Ohio State University Research Foundation -DO NOT USE
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1990
• 资助国家: 美国
• 起止时间: 1990-07-01 至 1994-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8922721&HistoricalAwards=false
• 关键词: Multiple; Comparisons; Optimization; via; Simulation

The research will focus on derivations of statistically efficient multiple-comparison procedures for optimization via stochastic simulation. The results will be applicable when the objective is to maximize or minimize expected system performance with respect to a discrete, finite decision space. Such problems are encountered when a number of alternative system designs arise from combinations of equipment, schedules or facilities, subject to constraints on the available budget or technology. Multiple- comparison procedures are the natural approach for optimization in such design problems. Two major efforts concentrate on basic methods for multiple comparisons: (1) multiple-comparison inference, and thus be able to distinguish small differences in expected system performance and (2) derivations of multiple- comparison procedures for steady-state simulation experiements. Results of this research should aid in making Monte Carlo simulation, a most important tool for analysis of complex systems, more efficient and useful.

研究将集中在统计的推导 用于优化的有效多重比较程序， 随机模拟 结果将适用时， 目标是最大化或最小化预期的系统性能 对于离散的有限决策空间。 此类问题 当出现许多替代系统设计时， 从设备、时间表或设施的组合中， 对现有预算或技术的限制。 多重- 比较程序是优化的自然方法， 这样的设计问题。 两大着力点集中在基础 多重比较的方法：（1）多重比较 推理，从而能够区分小的差异， 预期的系统性能和（2）多个- 稳态模拟实验的比较程序。 这项研究的结果应该有助于使蒙特卡罗 仿真是分析复杂系统最重要的工具， 更有效和有用。