Supervised Machine Learning for Understanding and Improving the Computational Performance of Chemical Production Scheduling MIP Models

We adopt a supervised learning approach to predict runtimes of batch production scheduling mixed-integer programming (MIP) models with the aim of understanding what instance features make a model computationally expensive. We introduce novel features to characterize instance difficulty according to problem type. The developed machine learning models trained on runtime data obtained from a wide variety of instances show good predictive performances. Then, we discuss informative features and their effects on computational performance. Finally, based on the derived insights, we propose solution methods for improving the computational performance of batch scheduling MIP models.

我们采用监督学习方法，对批量生产调度混合整数规划（mixed-integer programming, MIP）模型的求解时长开展预测，旨在探究哪些实例特征会导致模型的计算成本居高不下。针对不同问题类型，我们提出了用于刻画实例求解难度的全新特征。基于多类实例获取的求解时长数据训练得到的机器学习模型，展现出了优异的预测性能。随后，我们对具备信息价值的特征及其对计算性能的影响展开了深入讨论。最后，基于本研究推导得到的核心洞察，我们提出了可提升批量调度混合整数规划模型计算性能的求解方法。