Structured Simulation Optimization and Analysis

结构化仿真优化与分析

• 批准号: 0400287
• 负责人: Shane Henderson
• 金额: $ 29.97万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-01 至 2008-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0400287&HistoricalAwards=false
• 关键词: Structured; Simulation; Optimization; Analysis

A huge number of applications involve simulation optimization, i.e., the optimization of a function that is computed via Monte Carlo simulation. The primary goal of the research is to develop solution methods for structured simulation optimization problems. The word "structured" suggests that the problem possesses certain structural properties that can be leveraged by optimization algorithms to, for example, reduce solution time or improve the likelihood of convergence. In more detail, the primary goals of this research are as follows. First, to further develop simulation optimization methods for structured problems, where the structure may be detected and verified with minimal user input. Second, to apply those methods on a range of practical problems, with emphasis on emergency services in rural areas. Third, to create a library of simulation optimization problems that can be used to help compare proposed optimization methods, and help guide the development of new simulation-optimization algorithms.The development of structured simulation-optimization methods will allow far larger models and associated optimization problems to be solved than is possible today. The potential list of applications is enormous, since Monte Carlo simulation is now used in a tremendous range of fields including, but certainly not limited to, emergency service planning, manufacturing, communications systems, service system design and health care. The research explicitly includes applications to emergency service planning in rural areas, with the goal of obtaining more effective response times and coverage. Other potential benefits include improved methods for operating policy design for networks such as arise in power systems, communication systems and manufacturing. Applications also exist in health care, such as in external beam radiation treatment planning for cancer patients.

大量的应用涉及模拟优化，即通过蒙特卡罗模拟计算的函数的优化。研究的主要目标是开发结构化仿真优化问题的求解方法。“结构化”一词表明问题具有某些结构性属性，优化算法可以利用这些属性来减少求解时间或提高收敛的可能性。更详细地说，本研究的主要目标如下。首先，进一步开发结构化问题的模拟优化方法，其中可以用最少的用户输入来检测和验证结构。第二，将这些方法应用于一系列实际问题，重点是农村地区的紧急服务。第三，创建一个模拟优化问题库，用来帮助比较所提出的优化方法，并帮助指导新的模拟优化算法的开发。结构化模拟优化方法的发展将允许解决比目前可能的更大的模型和相关的优化问题。潜在的应用清单是巨大的，因为蒙特卡洛模拟现在被用于广泛的领域，包括但当然不限于应急服务规划、制造、通信系统、服务系统设计和医疗保健。这项研究明确包括应用于农村地区的应急服务规划，目的是获得更有效的反应时间和覆盖范围。其他潜在的好处包括改进了电力系统、通信系统和制造业等网络的运营策略设计方法。在医疗保健方面也存在应用，例如在癌症患者的体外放射治疗计划中。