Online Scheduling

Online scheduling focuses on dynamically assigning tasks to resources, aiming to optimize performance metrics like resource utilization, task completion time, and cost-effectiveness across diverse applications. Current research heavily utilizes reinforcement learning, particularly deep reinforcement learning, and other machine learning techniques like bandit algorithms and neuro-fuzzy systems, often incorporating constraint-based approaches to handle real-world limitations. These advancements are improving efficiency and resource management in cloud computing, human-robot collaboration, and other domains with complex, dynamic task allocation needs, leading to significant improvements in performance and user experience.