Dynamic Scheduling

Dynamic scheduling optimizes the allocation of resources to tasks over time, aiming to maximize efficiency and meet performance goals in dynamic environments. Current research heavily utilizes reinforcement learning (RL), particularly deep RL and multi-agent RL, along with techniques like learning to rank and constraint programming, to develop adaptive scheduling policies for diverse applications. These advancements are impacting various fields, including manufacturing, large language model serving, federated learning, and resource-constrained systems like those in smart grids and vehicular networks, by improving resource utilization and performance. The development of explainable and system-agnostic scheduling methods is also a growing area of focus.