Collaborative Research: A Framework for Effective Optimization via Simulation

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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.