“However, the computational power needed to run that optimization model at a statewide level was not practical. To resolve this challenge, the researchers took a novel approach: combining machine learning with a technique called column generation. Column generation is a longstanding method used to address optimization challenges involving large numbers of variables. Researchers found that using this machine learning-guided column generation (ML-CG) approach accelerated run-time for their vaccine optimization model by 79.1% compared to pure column generation – while still providing high-quality solutions. The paper, “Machine Learning-Guided Column Generation for a Maximal Covering Location-Allocation Problem,” is published open access in the journal Sustainability Analytics and Modeling.