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

Abstract

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.