Joint chance constrained probabilistic simple temporal networks via column generation (extended abstract)

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Murray, Andrew and Cashmore, Michael and Arulselvan, Ashwin (2022) Joint chance constrained probabilistic simple temporal networks via column generation (extended abstract). In: 19th International Conference on the Integration of Constraint Programmng, Artificial Intelligence, and Operations Research, 2022-06-20 - 2022-06-23, https://sites.google.com/usc.edu/cpaior-2022.

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Abstract

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. In this paper we introduce the Joint Chance-Constrained PSTN (JCC-PSTN) which lifts assumptions of independence and Boole's inequality, which are typically leveraged in PSTN literature. We solve the problem of JCC-PSTN SC via a column generation procedure and find that our approach offers on average a 10 times reduction in cost versus using Boole’s inequality.

ORCID iDs

Murray, Andrew, Cashmore, Michael ORCID logoORCID: https://orcid.org/0000-0002-8334-4348 and Arulselvan, Ashwin ORCID logoORCID: https://orcid.org/0000-0001-9772-5523;
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