Scheduling Problem

Scheduling problems, encompassing the optimization of resource allocation over time, aim to minimize costs, maximize efficiency, and meet various constraints across diverse applications. Current research heavily utilizes machine learning, particularly deep reinforcement learning and graph neural networks, alongside established methods like genetic algorithms, simulated annealing, and column generation, often hybridized for improved performance. These advancements are significantly impacting fields like manufacturing, logistics, and transportation by enabling more efficient resource utilization and improved decision-making in complex, real-world scenarios. The development of explainable AI methods for scheduling solutions is also a growing area of focus.