Learning From Scenarios for Stochastic Repairable Scheduling
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866910565635457024 |
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| author | Houten, Kim van den Tax, David M. J. Freydell, Esteban de Weerdt, Mathijs |
| author_facet | Houten, Kim van den Tax, David M. J. Freydell, Esteban de Weerdt, Mathijs |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2312_03492 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Learning From Scenarios for Stochastic Repairable Scheduling Houten, Kim van den Tax, David M. J. Freydell, Esteban de Weerdt, Mathijs Machine Learning Artificial Intelligence 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. |
| title | Learning From Scenarios for Stochastic Repairable Scheduling |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2312.03492 |