Joint Scheduling of Deferrable and Nondeferrable Demand with Colocated Stochastic Supply

We address the problem of optimal joint scheduling of deferrable and nondeferrable demand involving colocated stochastic supply. Deferrable demand can be delayed within its service deadline, whereas nondeferrable demand must be scheduled immediately. Under a finite-horizon stochastic dynamic programming formulation, we show that the optimal scheduling policy is a ``procrastination policy'' that delays scheduling as much as possible and is characterized by three procrastination parameters. Exploiting the low-dimensional parameterization of the optimal policy, we propose a Procrastination Threshold Reinforcement Learning algorithm. Numerical experiments based on real-world test data confirm that the threshold-learning algorithm closely approximates the optimal policy and outperforms standard benchmarks.