Collaborative Research: A Framework for Effective Optimization via Simulation

协作研究：通过模拟进行有效优化的框架

• 批准号: 0217860
• 负责人: Sigrun Andradottir
• 金额: $ 20万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-08-01 至 2006-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0217860&HistoricalAwards=false
• 关键词: Collaborative; Research; Framework; Effective; Optimization

We will develop algorithms (and supporting theory) for optimizing the expected performance of a stochastic system with respect to discrete decision variables. We assume that the stochastic system of interest is represented by a simulation model, and hence that the performance of this system can only be estimated with noise. Our focus is on ``general-purpose'' optimization techniques that do not exploit particular problem structure, because we want our techniques to be suitable for inclusion in general-purpose simulation software. The goal is to produce algorithms that have provable asymptotic performance, competitive finite-time performance, and valid statistical inference at termination. The keys to our approach are (1) our algorithms will work within a global guidance framework that guarantees asymptotic convergence, while giving us wide latitude to be aggressive and adaptive; (2) within this framework, we will embed aggressive local-improvement schemes; (3) we will enhance the local-improvement schemes with highly efficient selection-error control to insure improvement even in the presence of estimation error; and (4) we will provide valid statistical inference at algorithm termination so that the solution reported as best will be the best, or near best, of all those solutions actually visited by the search, with a prespecified confidence level.In the United States, computer simulation is widely used to design and improve ("optimize") manufacturing, service, military, telecommunication and financial systems that are subject to uncertainty. Our research will provide theoretically sound optimization algorithms that can be incorporated into new or existing simulation software packages. There is a critical need for this research, because every day simulation users are formulating and attempting to solve optimization-via-simulation problems using commercial products that ignore, or only slightly notice, that the simulation experiment incorporates uncertainty. These commercial products often work well, but they can also be dramatically misled, and the user has no indication of, or protection against, the incorrect and costly decisions that may result. The availability of optimization tools in nearly all commercial simulation modeling packages implies that optimization-via-simulation problems will be "solved." The question is whether they will be solved efficiently with theoretically sound algorithms that provide specific guarantees of, and inference on, their performance. The goal of our research is to develop such optimization-via-simulation algorithms, representing a substantial advance over the state of the art in both theory and practice.

我们将开发算法（和支持理论），以优化离散决策变量的随机系统的预期性能。 我们假设感兴趣的随机系统由模拟模型表示，因此只能用噪声估算该系统的性能。 我们的重点是不利用特定问题结构的``通用''优化技术，因为我们希望我们的技术适合包含在通用模拟软件中。目的是生产具有可证明的渐近性能，有限的时间性能以及终止时有效统计推断的算法。我们方法的关键是（1）我们的算法将在一个全球指导框架内发挥作用，该框架可以保证渐近融合，同时为我们提供广泛的纬度，使其具有侵略性和适应性； （2）在此框架内，我们将嵌入积极的局部改善方案； （3）我们将增强具有高效的选择 - 纠正控制的本地改进方案，即使在存在估计误差的情况下，也可以确保改进； （4）我们将在算法终止上提供有效的统计推论，以便在搜索实际访问的所有解决方案中，最佳或接近最好的解决方案，具有预先指定的信心级别。计算机模拟广泛用于设计和改进（“优化”）制造，服务，军事，电信和财务系统，这些系统受到不确定性的影响。我们的研究将提供理论上可以合并到新的或现有的仿真软件包中的声音优化算法。这项研究非常需要，因为每天模拟用户都在制定并试图使用忽略或仅稍微注意到的商业产品解决优化 - 仿真问题，即模拟实验融合了不确定性。 这些商业产品通常运行良好，但也可能会大大误导，用户没有迹象表明或防止可能导致的错误和昂贵的决定。 优化工具在几乎所有商业仿真建模包中的可用性都意味着优化 - 仿真问题将“解决”。问题是，是否将使用理论上声音算法有效地解决它们，这些算法可以提供特定的保证和推断其性能。 我们研究的目的是开发这种优化 - 视觉模拟算法，这代表了理论和实践中对最新技术的实质性进步。