Learning from Scenarios for Repairable Stochastic Scheduling
Kim van den Houten (TU Delft - Algorithmics)
DMJ Tax (TU Delft - Pattern Recognition and Bioinformatics)
Esteban Freydell (DSM)
Mathijs M. de Weerdt (TU Delft - Algorithmics)
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Abstract
When optimizing problems with uncertain parameter values in a linear objective, decision-focused learning enables end-to-end learning of these values. We are interested in a stochastic scheduling problem, in which processing times are uncertain, which brings uncertain values in the constraints, and thus repair of an initial schedule may be needed. Historical realizations of the stochastic processing times are available. We show how existing decision-focused learning techniques based on stochastic smoothing can be adapted to this scheduling problem. We include an extensive experimental evaluation to investigate in which situations decision-focused learning outperforms the state of the art, i.e., scenario-based stochastic optimization.