Learning From Scenarios for Stochastic Repairable Scheduling

• URL: http://arxiv.org/abs/2312.03492v2
• Date: Thu, 15 Aug 2024 13:21:26 GMT
• Title: Learning From Scenarios for Stochastic Repairable Scheduling
• Authors: Kim van den Houten, David M. J. Tax, Esteban Freydell, Mathijs de Weerdt,
• Abstract summary: We show how decision-focused learning techniques based on smoothing can be adapted to a scheduling problem. We include an experimental evaluation to investigate in which situations decision-focused learning outperforms the state of the art for such situations: scenario-based optimization.
• Score: 3.9948520633731026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 for such situations: scenario-based stochastic optimization.

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