标题： 可调度与不可调度需求的联合调度与同址随机供应 显示英文标题

标题： Joint Scheduling of Deferrable and Nondeferrable Demand with Colocated Stochastic Supply

摘要： 我们解决涉及共址随机供应的可延迟和不可延迟需求的最优联合调度问题。 可延迟需求可以在其服务截止时间范围内延迟，而不可延迟需求必须立即安排。 在有限时域的随机动态规划框架下，我们证明最优调度策略是一种“拖延策略”，即尽可能推迟调度，并由三个拖延参数表征。 利用最优策略的低维参数化，我们提出了一种拖延阈值强化学习算法。 基于真实世界测试数据的数值实验确认，该阈值学习算法能够紧密逼近最优策略，并优于标准基准。 显示英文摘要

摘要： 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.