Collaborative Research: A Framework for Effective Optimization via Simulation

我们将开发算法（和支持理论），用于优化随机系统相对于离散决策变量的预期性能。 我们假设的随机系统的兴趣是由一个模拟模型，因此，该系统的性能只能估计噪声。 我们的重点是“通用”的优化技术，不利用特定的问题结构，因为我们希望我们的技术是适合列入通用模拟软件。我们的目标是产生算法，具有可证明的渐近性能，有竞争力的有限时间性能，并在终止时有效的统计推断。我们的方法的关键是（1）我们的算法将工作在一个全局指导框架，保证渐近收敛，同时给我们很大的自由是积极的和自适应的;（2）在这个框架内，我们将嵌入积极的局部改进计划;（3）我们将加强局部改进计划与高效的选择错误控制，以确保即使在存在估计错误的情况下的改进;（4）在算法结束时，我们将提供有效的统计推断，使得报告为最佳的解将是搜索实际访问的所有那些解中的最佳或接近最佳的解，具有预先指定的置信水平。（"优化"）受不确定性影响的制造业、服务业、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，可以纳入新的或现有的仿真软件包。有一个迫切需要这项研究，因为每天仿真用户制定并试图解决优化通过仿真问题，使用商业产品忽略，或只是稍微注意到，仿真实验包含不确定性。 这些商业产品通常运行良好，但它们也可能被严重误导，用户没有任何迹象表明或保护，可能导致不正确和昂贵的决定。 几乎所有商业仿真建模软件包中的优化工具的可用性意味着通过仿真进行优化的问题将被"解决"。"问题是，它们是否会被有效地解决与理论上健全的算法，提供具体的保证，并推断，他们的表现。 我们的研究的目标是开发这样的优化，通过模拟算法，代表了一个实质性的进步，在理论和实践上的最先进的。

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.