Extrapolating from limited uncertain information in large-scale combinatorial optimization problems to obtain robust solutions

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dc.contributor.authorCliment, Laura
dc.contributor.authorWallace, Richard J.
dc.contributor.authorO'Sullivan, Barry
dc.contributor.authorFreuder, Eugene C.
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2021-04-27T10:39:38Z
dc.date.available2021-04-27T10:39:38Z
dc.date.issued2016-02
dc.date.updated2021-04-27T10:30:59Z
dc.descriptionThis paper is an extended version of L. Climent et al. in Proceedings of ICTAI-2014. International Journal On Artificial Intelligence Tools, 25 (1) Special Issue on 26th IEEE International Conference on Tools with Artificial Intelligence (ICTAI-2014); Guest Editor: George Angelos Papadopoulos
dc.description.abstractData uncertainty in real-life problems is a current challenge in many areas, including Operations Research (OR) and Constraint Programming (CP). This is especially true given the continual and accelerating increase in the amount of data associated with real life problems, to which Large Scale Combinatorial Optimization (LSCO) techniques may be applied. Although data uncertainty has been studied extensively in the literature, many approaches do not take into account the partial or complete lack of information about uncertainty in real-life settings. To meet this challenge, in this paper we present a strategy for extrapolating data from limited uncertain information to ensure a certain level of robustness in the solutions obtained. Our approach is motivated and evaluated with real-world applications of harvesting and supplying timber from forests to mills and the well known knapsack problem with uncertainty.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationCliment, L., Wallace, R. J., O'Sullivan, B. and Freuder, E. C. (2016) 'Extrapolating from Limited Uncertain Information in Large-Scale Combinatorial Optimization Problems to Obtain Robust Solutions', International Journal On Artificial Intelligence Tools, 25 (01), doi: 10.1142/S0218213016600058en
dc.identifier.doi10.1142/S0218213016600058en
dc.identifier.eissn1793-6349
dc.identifier.endpage20en
dc.identifier.issn0218-2130
dc.identifier.issued01en
dc.identifier.journaltitleInternational Journal On Artificial Intelligence Toolsen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/11224
dc.identifier.volume25en
dc.language.isoenen
dc.publisherWorld Scientific Publishingen
dc.relation.ispartof2014 IEEE 26th International Conference on Tools with Artificial Intelligence (ICTAI) Nov. 10 2014 to Nov. 12 2014, Limassol, Cyprusen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.relation.urihttps://www.worldscientific.com/doi/pdf/10.1142/S0218213016600058
dc.rights© 2016 World Scientific Publishing Company. This is the accepted version of an article published in International Journal on Artificial Intelligence Tools Vol. 25, No. 01, https://www.worldscientific.com/doi/pdf/10.1142/S0218213016600058en
dc.subjectUncertaintyen
dc.subjectRobustnessen
dc.subjectOptimizationen
dc.titleExtrapolating from limited uncertain information in large-scale combinatorial optimization problems to obtain robust solutionsen
dc.typeArticle (peer-reviewed)en
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