The field of scheduling and optimization is moving towards more complex and realistic models, taking into account non-stationary environments, interdependencies between tasks, and imperfect predictions. Researchers are developing new algorithms and policies that can handle these challenges, such as Markovian Service Rate policies, influential bandits, and robust Gittins index policies. These innovative approaches aim to improve the performance and efficiency of scheduling systems, even in the presence of uncertainty and mispredictions. Notable papers in this area include: Influential Bandits: Pulling an Arm May Change the Environment, which proposes a new algorithm that achieves a nearly optimal regret bound. Robust Gittins for Stochastic Scheduling, which modifies the Gittins index policy to make it robust against mispredictions.