@inproceedings{MTMT:34852926,
	title = {A Column Generation Approach to Correlated Simple Temporal Networks},
	url = {https://m2.mtmt.hu/api/publication/34852926},
	author = {Murray, Andrew and Arulselvan, Ashwin and Cashmore, Michael and Roper, Marc and Frank, Jeremy},
	booktitle = {Proceedings of the Thirty-Third International Conference on Automated Planning and Scheduling},
	doi = {10.1609/icaps.v33i1.27207},
	unique-id = {34852926},
	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.},
	year = {2023},
	pages = {295-303}
}

@article{MTMT:32577173,
	title = {A derivative-free trust-region algorithm with copula-based models for probability maximization problems},
	url = {https://m2.mtmt.hu/api/publication/32577173},
	author = {Butyn, Emerson and Karas, Elizabeth W. and de Oliveira, Welington},
	doi = {10.1016/j.ejor.2021.09.040},
	journal-iso = {EJOR},
	journal = {EUROPEAN JOURNAL OF OPERATIONAL RESEARCH},
	volume = {298},
	unique-id = {32577173},
	issn = {0377-2217},
	year = {2022},
	eissn = {1872-6860},
	pages = {59-75},
	orcid-numbers = {Butyn, Emerson/0000-0001-9930-2516}
}

@article{MTMT:33337865,
	title = {On the Convexity of Level-Sets of Probability Functions},
	url = {https://m2.mtmt.hu/api/publication/33337865},
	author = {Laguel, Yassine and van Ackooij, Wim and Malick, Jerome and Ramalho, Guilherme Matiussi},
	journal-iso = {J CONVEX ANAL},
	journal = {JOURNAL OF CONVEX ANALYSIS},
	volume = {29},
	unique-id = {33337865},
	issn = {0944-6532},
	abstract = {In decision-making problems under uncertainty, probabilistic constraints are a valuable tool to express safety of decisions. They result from taking the probability measure of a given set of random inequalities depending on the decision vector. Even if the original set of inequalities is convex, this favourable property is not immediately transferred to the probabilistically constrained feasible set and may in particular depend on the chosen safety level. In this paper, we provide results guaranteeing the convexity of feasible sets to probabilistic constraints when the safety level is greater than a computable threshold. Our results extend all the existing ones and also cover the case where decision vectors belong to Banach spaces. The key idea in our approach is to reveal the level of underlying convexity in the nominal problem data (e.g., concavity of the probability function) by auxiliary transforming functions. We provide several examples illustrating our theoretical developments.},
	keywords = {Stochastic optimization; Convex analysis; Probability constraints; Elliptical distributions},
	year = {2022},
	eissn = {0944-6532},
	pages = {411-442}
}

@article{MTMT:33542144,
	title = {Joint Chance Constrained Probabilistic Simple Temporal Networks via Column Generation},
	url = {https://m2.mtmt.hu/api/publication/33542144},
	author = {Murray, Andrew and Cashmore, Michael and Arulselvan, Ashwin and Frank, Jeremy},
	doi = {10.1609/socs.v15i1.21794},
	journal-iso = {SOCS},
	journal = {Proceedings of the International Symposium on Combinatorial Search},
	volume = {15},
	unique-id = {33542144},
	issn = {2832-9171},
	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. 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.},
	year = {2022},
	eissn = {2832-9163},
	pages = {305-307}
}

@inproceedings{MTMT:32817617,
	title = {Towards temporally uncertain explainable AI planning},
	url = {https://m2.mtmt.hu/api/publication/32817617},
	author = {Murray, Andrew and Krarup, Benjamin and Cashmore, Michael},
	booktitle = {Distributed Computing and Intelligent Technology: 18th International Conference, ICDCIT 2022},
	doi = {10.1007/978-3-030-94876-4_3},
	unique-id = {32817617},
	year = {2022},
	pages = {45-59}
}

@inbook{MTMT:32601778,
	title = {Fogyasztás ütemezési probléma egyoldalas együttes valószínűséggel korlátozott sztochasztikus modellje. A stochastic model of the one-sided joint probability constrained consumption scheduling problem},
	url = {https://m2.mtmt.hu/api/publication/32601778},
	author = {Drenyovszki, Rajmund},
	booktitle = {Kutatás és innováció 2021},
	unique-id = {32601778},
	year = {2021},
	pages = {389-394}
}

@article{MTMT:31387026,
	title = {Gaining traction: on the convergence of an inner approximation scheme for probability maximization},
	url = {https://m2.mtmt.hu/api/publication/31387026},
	author = {Fábián, Csaba},
	doi = {10.1007/s10100-020-00697-3},
	journal-iso = {CEJOR},
	journal = {CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH},
	volume = {29},
	unique-id = {31387026},
	issn = {1435-246X},
	year = {2020},
	eissn = {1613-9178},
	pages = {491-519},
	orcid-numbers = {Fábián, Csaba/0000-0002-9446-1566}
}

@article{MTMT:31609283,
	title = {A Discussion of Probability Functions and Constraints from a Variational Perspective},
	url = {https://m2.mtmt.hu/api/publication/31609283},
	author = {van Ackooij, Wim},
	doi = {10.1007/s11228-020-00552-2},
	journal-iso = {SET-VALUED VAR ANAL},
	journal = {SET-VALUED AND VARIATIONAL ANALYSIS},
	volume = {28},
	unique-id = {31609283},
	issn = {1877-0533},
	year = {2020},
	eissn = {1877-0541},
	pages = {585-609}
}

@article{MTMT:30415406,
	title = {A randomized method for handling a difficult function in a convex optimization problem, motivated by probabilistic programming},
	url = {https://m2.mtmt.hu/api/publication/30415406},
	author = {Fábián, Csaba and Gurka Dezsőné Csizmás, Edit Margit and Drenyovszki, Rajmund and Vajnai, Tibor and Kovács, Lóránt and Szántai, Tamás},
	doi = {10.1007/s10479-019-03143-z},
	journal-iso = {ANN OPER RES},
	journal = {ANNALS OF OPERATIONS RESEARCH},
	volume = {Online first},
	unique-id = {30415406},
	issn = {0254-5330},
	year = {2019},
	eissn = {1572-9338},
	pages = {1-32},
	orcid-numbers = {Fábián, Csaba/0000-0002-9446-1566; Gurka Dezsőné Csizmás, Edit Margit/0000-0003-4397-1758; Drenyovszki, Rajmund/0000-0002-9462-2729}
}

@mastersthesis{MTMT:33558585,
	title = {Advances in stochastic programming approaches to optimization under uncertainty},
	url = {https://m2.mtmt.hu/api/publication/33558585},
	author = {Martin, Branda},
	unique-id = {33558585},
	year = {2019}
}

@article{MTMT:30879100,
	title = {Sharp upper and lower bounds for maximum likelihood solutions to random Gaussian bilateral inequality systems},
	url = {https://m2.mtmt.hu/api/publication/30879100},
	author = {Minoux, Michel and Zorgati, Riadh},
	doi = {10.1007/s10898-019-00756-3},
	journal-iso = {J GLOBAL OPTIM},
	journal = {JOURNAL OF GLOBAL OPTIMIZATION},
	volume = {75},
	unique-id = {30879100},
	issn = {0925-5001},
	year = {2019},
	eissn = {1573-2916},
	pages = {735-766}
}