Extrapolating from limited uncertain information to obtain robust solutions for large-scale optimization problems
| dc.contributor.author | Climent, Laura | en |
| dc.contributor.author | Wallace, Richard | en |
| dc.contributor.author | O'Sullivan, Barry | en |
| dc.contributor.author | Freuder, Eugene | en |
| dc.contributor.funder | Science Foundation Ireland | en |
| dc.date.accessioned | 2025-01-08T12:29:52Z | |
| dc.date.available | 2025-01-08T12:29:52Z | |
| dc.date.issued | 2014-12-15 | en |
| dc.description.abstract | Data 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 by real-world applications of supply of timber from forests to saw-mills. | en |
| dc.description.status | Peer reviewed | en |
| dc.description.version | Accepted Version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Climent, L., Wallace, R., O'Sullivan, B. and Freuder, E. (2014) 'Extrapolating from limited uncertain information to obtain robust solutions for large-scale optimization problems', 2014 IEEE 26th International Conference on Tools with Artificial Intelligence, Limassol, Cyprus, 10-12 November 2014, pp. 898-905. https://doi.org/10.1109/ICTAI.2014.137 | en |
| dc.identifier.doi | 10.1109/ICTAI.2014.137 | en |
| dc.identifier.eissn | 2375-0197 | en |
| dc.identifier.endpage | 905 | en |
| dc.identifier.issn | 1082-3409 | en |
| dc.identifier.startpage | 898 | en |
| dc.identifier.uri | https://hdl.handle.net/10468/16791 | |
| dc.language.iso | en | en |
| dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en |
| dc.relation.ispartof | 2014 IEEE 26th International Conference on Tools with Artificial Intelligence, Limassol, Cyprus, 10-12 November 2014 | en |
| dc.relation.project | info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/ | en |
| dc.rights | © 2014, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en |
| dc.subject | Uncertainty | en |
| dc.subject | Robustness | en |
| dc.subject | Optimization | en |
| dc.title | Extrapolating from limited uncertain information to obtain robust solutions for large-scale optimization problems | en |
| dc.type | Conference item | en |
