Simulation optimization is a tool commonly used as a decision-making support system on industrial problems in order to find the best resource allocation, which has a direct influence on costs and revenues. The present study proposed an open-source framework developed on Python, integrating different strategies for a novel optimization algorithm. The framework includes multicore parallelism (tested on two different types of computer sets), (two) population-based metaheuristics, and 33 machine learning methods. Moreover, the study tested the framework to optimize resource allocation on a theoretical shop floor case study, evaluating 12 optimization scenarios. The use of metaheuristic with parallelism reduced 88.3% the processing time compared with the serial metaheuristic, while the integration of metaheuristic with the selected machine learning generated an additional reduction of 59.0% on the necessary processing time. The combination of the optimization methods created a solution of 95.3% near the global optimum and time reduction of 95.2%.

仿真优化是一种工具，通常用作针对工业问题的决策支持系统，以找到最佳的资源分配，这直接影响成本和收入。本研究提出了一个在Python上开发的开源框架，该框架集成了针对一种新型优化算法的不同策略。该框架包括多核并行性（在两种不同类型的计算机集上进行了测试），（两种）基于人口的元启发法和33种机器学习方法。此外，该研究在理论车间案例中测试了优化资源分配的框架，评估了12种优化方案。与串行元启发式方法相比，将元启发式方法与并行性结合使用可减少88.3％的处理时间，而将元启发法与选定的机器学习集成在一起，则在必要的处理时间上又减少了59.0％。优化方法的组合创造了接近全局最优的95.3％的解决方案和95.2％的时间减少。