The Exploding Domain of Simulation Optimization
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State-of-the-Art Decision Making Techniques
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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More Optimization White Papers
Tabu Search, also called Adaptive Memory Programming, is a method for solving challenging optimization problems in the fields of business, engineering, economics and science. Everyday examples include practical applications in resource management, financial and investment planning, healthcare systems, energy and environmental policy, pattern classification, biotechnology and a host of other areas. Tabu search has emerged as one of the leading technologies for handling real-world problems that have proved difficult or impossible to solve with classical procedures.
Optimizing AI/ML Hyperparameters with SimWrapper and OptQuest
Download our report to see how SimWrapper and OptQuest can be used to optimally tune hyperparameters for your AI/ML models.