A column generation approach to correlated simple temporal networks
Murray, Andrew and Arulselvan, Ashwin and Cashmore, Michael and Roper, Marc and Frank, Jeremy; Koenig, Sven and Stern, Roni and Vallati, Mauro, eds. (2023) A column generation approach to correlated simple temporal networks. In: Proceedings of the Thirty-Third International Conference on Automated Planning and Scheduling. Proceedings of the International Conference on Automated Planning and Scheduling . AAAI Press, CZE, pp. 295-303. ISBN 9781577358817 (https://doi.org/10.1609/icaps.v33i1.27207)
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Abstract
Probabilistic Simple Temporal Networks (PSTN) represent scheduling problems under temporal uncertainty. Strong controllability (SC) of PSTNs involves finding a schedule to a PSTN that maximises the probability that all constraints are satisfied (robustness). Previous approaches to this problem assume independence of probabilistic durations, and approximate the risk by bounding it above using Boole’s inequality. This gives no guarantee of finding the schedule optimising robustness, and fails to consider correlations between probabilistic durations that frequently arise in practical applications. In this paper, we formally define the Correlated Simple Temporal Network (Corr-STN) which generalises the PSTN by removing the restriction of independence. We show that the problem of Corr-STN SC is convex for a large class of multivariate (log-concave) distributions. We then introduce an algorithm capable of finding optimal SC schedules to Corr-STNs, using the column generation method. Finally, we validate our approach on a number of Corr-STNs and find that our method offers more robust solutions when compared with prior approaches.
ORCID iDs
Murray, Andrew, Arulselvan, Ashwin ORCID: https://orcid.org/0000-0001-9772-5523, Cashmore, Michael ORCID: https://orcid.org/0000-0002-8334-4348, Roper, Marc ORCID: https://orcid.org/0000-0001-6794-4637 and Frank, Jeremy; Koenig, Sven, Stern, Roni and Vallati, Mauro-
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Item type: Book Section ID code: 87874 Dates: DateEvent1 July 2023Published4 February 2023AcceptedSubjects: Science > Mathematics > Electronic computers. Computer science
Science > Mathematics > Probabilities. Mathematical statisticsDepartment: Faculty of Science > Computer and Information Sciences
Strategic Research Themes > Health and Wellbeing
Strategic Research Themes > Measurement Science and Enabling Technologies
Strathclyde Business School > Management ScienceDepositing user: Pure Administrator Date deposited: 23 Jan 2024 14:42 Last modified: 22 Dec 2024 01:09 URI: https://strathprints.strath.ac.uk/id/eprint/87874