The recent developments in the research area demonstrate a significant shift towards leveraging advanced computational techniques and machine learning methodologies to address complex optimization and scheduling problems. A notable trend is the integration of large language models (LLMs) for configuring optimization solvers, which offers a novel approach to handle the vast parameter space in mixed integer linear programming (MILP) solvers. This method not only simplifies the configuration process but also enhances performance through innovative ensembling strategies. Additionally, there is a growing focus on multi-task representation learning for MILP, which aims to improve scalability and adaptability by creating embeddings that guide solver performance across various tasks and solvers. In the realm of scheduling, integer linear programming models are being refined to optimize periodic scheduling in diverse service-oriented domains, showcasing their versatility and computational efficiency. Symmetry exploitation in minimal unsatisfiable subset (MUS) computation is another area witnessing advancements, with new techniques significantly reducing runtime for symmetric problems. Furthermore, the field is seeing innovative approaches to constraint acquisition, with a novel method for generalizing constraint models that enhances the modeling process's flexibility and robustness. Overall, these developments highlight a move towards more intelligent, adaptable, and efficient solutions in optimization and scheduling.