The recent developments in real-time scheduling and resource management have shown a significant shift towards more flexible and probabilistic models. Researchers are increasingly focusing on optimizing execution times and resource availability in dynamic environments, leveraging advanced mathematical formulations and heuristics. Notably, there is a growing interest in integrating packing algorithms with scheduling problems, particularly in scenarios involving harmonic periods and high processor utilization. Additionally, the modeling of intermittent resource availability and multitasking behavior in business processes has seen advancements through probabilistic approaches, enhancing the accuracy of simulation models. The field is also witnessing innovative techniques for scheduling parallel DAG tasks on multiprocessors, aiming to maximize processor efficiency while ensuring real-time constraints. Overall, the trend is towards more adaptive, efficient, and realistic scheduling algorithms that can handle complex, dynamic environments.