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.