Joint Chance Constrained Probabilistic Simple Temporal Networks via Column Generation (Extended Abstract)
| Autor: | Andrew Murray, Michael Cashmore, Ashwin Arulselvan, Jeremy Frank |
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| Rok vydání: | 2022 |
| Zdroj: | Proceedings of the International Symposium on Combinatorial Search. 15:305-307 |
| ISSN: | 2832-9163 2832-9171 |
| DOI: | 10.1609/socs.v15i1.21794 |
| Popis: | Probabilistic Simple Temporal Networks (PSTN) are used to represent scheduling problems under uncertainty. In a temporal network that is Strongly Controllable (SC) there exists a concrete schedule that is robust to any uncertainty. We solve the problem of determining Chance Constrained PSTN SC as a Joint Chance Constrained optimisation problem via column generation, lifting the usual assumptions of independence and Boole's inequality typically leveraged in PSTN literature. Our approach offers on average a 10 times reduction in cost versus previous methods. |
| Databáze: | OpenAIRE |
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