Collaborative Research: A Framework for Effective Optimization via Simulation

我们将开发关于离散决策变量的优化随机系统预期性能的算法(和支持理论)。我们假设感兴趣的随机系统由一个仿真模型来表示，因此该系统的性能只能在有噪声的情况下被估计。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。目标是产生具有可证明的渐近性能、竞争的有限时间性能和在终止时有效的统计推断的算法。该方法的关键是(1)我们的算法将在保证渐近收敛的全局制导框架内工作，同时给我们更大的进取和自适应的空间；(2)在这个框架内，我们将嵌入积极的局部改进方案；(3)我们将通过高效的选择误差控制来增强局部改进方案，以确保即使在存在估计误差的情况下也能得到改善；(4)我们将在算法终止时提供有效的统计推断，以便报告为最佳的解决方案将是搜索实际访问的所有解决方案中最好的或接近最佳的，并具有预先指定的置信度。在美国，计算机模拟被广泛用于设计和改进受不确定性影响的制造、服务、军事、电信和金融系统。我们的研究将提供理论上合理的优化算法，可以整合到新的或现有的仿真软件包中。这项研究非常有必要，因为模拟用户每天都在使用商业产品来制定和尝试解决通过模拟进行优化的问题，而这些商业产品忽略了或只是轻微地注意到模拟实验包含了不确定性。这些商业产品通常运行良好，但它们也可能被严重误导，并且用户没有迹象表明或没有针对可能导致的错误和代价高昂的决策的保护。几乎所有商业模拟建模包中都有优化工具，这意味着通过模拟进行优化的问题将得到“解决”。问题是，这些问题是否会用理论上合理的算法有效地解决，这些算法为它们的性能提供具体的保证和推断。我们的研究目标是开发这种通过模拟进行优化的算法，无论是在理论上还是在实践中，都代表着比最先进的水平有实质性的进步。

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.