Maintenance work order records provide valuable insights into chemical plants and production efficiency. These records are manually created in computerized management systems for routine and emergency maintenance. However, since the records are manually created, recording errors are not uncommon. The resulting datasets are additionally imbalanced, i.e., they have significantly more instances of certain classes than other minority classes. It is very challenging to use such datasets for classification and prediction of future events. In this paper, we propose a modeling framework that uses derivative-free optimization (DFO) to optimize the performance of classification models based on datasets that may be imbalanced. We apply our modeling framework to 15 real-world work order datasets. We also evaluate ten mixed-integer box-bounded DFO solvers for their ability to optimize machine learning models from industrial datasets. Compared to standard solutions, our results show dramatic improvements in the prediction accuracies of the models.

维护工单记录为化工厂和生产效率提供了宝贵的见解。这些记录是在计算机化的管理系统中手动创建的，用于日常和紧急维护。但是，由于记录是手动创建的，因此记录错误并不少见。生成的数据集还会不平衡，即，与其他少数类相比，它们具有某些类的实例要多得多。使用此类数据集进行未来事件的分类和预测非常具有挑战性。在本文中，我们提出了一个建模框架，该框架使用无导数优化（DFO）基于可能不平衡的数据集优化分类模型的性能。我们将建模框架应用于15个实际工作订单数据集。我们还评估了十个混合整数，有边界的DFO求解器从工业数据集中优化机器学习模型的能力。与标准解决方案相比，我们的结果显示出模型预测准确性的显着提高。