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Shop floor simulation optimization using machine learning to improve parallel metaheuristics
Expert Systems with Applications ( IF 7.5 ) Pub Date : 2020-02-05 , DOI: 10.1016/j.eswa.2020.113272
Wilson Trigueiro de Sousa Junior , José Arnaldo Barra Montevechi , Rafael de Carvalho Miranda , Mona Liza Moura de Oliveira , Afonso Teberga Campos

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%.



中文翻译:

使用机器学习的车间模拟优化,以改善并行元启发式

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。

更新日期:2020-02-05
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