Online Resource Allocation

Online resource allocation focuses on dynamically distributing limited resources among competing demands, aiming to optimize overall efficiency and fairness. Current research emphasizes developing algorithms robust to uncertainty in arrival patterns and prediction accuracy, incorporating fairness metrics, and handling long-term constraints, often employing techniques like online learning (e.g., primal-dual methods, exponentially weighted algorithms), and reinforcement learning. These advancements are crucial for improving performance in diverse applications such as network management, revenue optimization, and sustainable energy systems, where efficient and equitable resource use is paramount.