Ensemble-Based Infill Search Simulation Optimization Framework

Simulation is widely used in several areas of knowledge, from engineering to biology, including physics and finance. It allows the evaluation of the model’s results under different conditions, enabling performance analysis and more assertive decision-making. However, simulation can be computationally intensive, especially when we consider complex models. To deal with this problem, metamodeling has been increasingly used as a simulation optimization technique. In this article, we propose a new adaptive metamodeling method for simulation optimization, which aims to achieve better results using fewer experiments. This method combines machine learning and metaheuristic techniques, allowing the identification of the most important regions of the search space, which can be explored more efficiently to obtain optimal solutions. The results achieved in a manufacturing problem show that the proposed method presents a significant improvement in the achieved objective function value, in comparison with the conventional benchmark method, without compromising the simulation execution time.