Scheduling Policy

Scheduling policy research aims to optimize the allocation of resources to tasks, minimizing costs and maximizing efficiency across diverse applications. Current research heavily utilizes reinforcement learning, often incorporating deep neural networks like self-attention models and graph neural networks, alongside constraint programming and evolutionary algorithms, to create adaptive and robust scheduling policies. These advancements are crucial for improving resource utilization and performance in areas such as cloud computing, manufacturing, and transportation, impacting both theoretical understanding of optimization problems and practical deployment of efficient systems.