Optimal scheduling of deferrable demands with colocated stochastic supply and piecewise-linear pricing reduces to a finite set of three procrastination thresholds per demand class; a reinforcement learning algorithm learns these thresholds when distributions are unknown.
Hence, by the first order condition of v, v∗ t = min{¯v, yt}, which is equivalent with θ− t (gt) = 0
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Joint Scheduling of Deferrable and Nondeferrable Demand with Colocated Stochastic Supply
Optimal scheduling of deferrable demands with colocated stochastic supply and piecewise-linear pricing reduces to a finite set of three procrastination thresholds per demand class; a reinforcement learning algorithm learns these thresholds when distributions are unknown.