Adaptive Scheduling

Adaptive scheduling optimizes the execution order of tasks or processes to improve efficiency and performance across diverse applications. Current research focuses on developing adaptive algorithms, often employing machine learning techniques like reinforcement learning and evolutionary algorithms, to dynamically adjust schedules based on real-time conditions and changing objectives, such as minimizing energy consumption or latency. These advancements are impacting various fields, from resource-constrained edge computing and IoT networks to complex manufacturing processes and space mission planning, enabling more efficient and robust systems.