The merging of optimization and simulation techniques has seen remarkable growth in recent year both in the academic world and practical settings. A principal reason underlying the importance of simulation optimization is that many real-world problems in optimization are too complex to be given tractable mathematical formulations. Multiple nonlinearities, combinatorial relationships and uncertainties often render challenging practical problems inaccessible to modeling except by resorting to simulation.
In this paper, we first summarize some of the most relevant approaches that have been developed for optimizing simulated systems. We then concentrate on the metaheuristic black-box approach that leads the field of practical applications and provide relevant details of how this approach has been implemented and used in commercial software. We close by presenting an example of simulation optimization in the context of a simulation model developed to predict performance and measure risk in a real-world project selection problem.
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