Please use this identifier to cite or link to this item: http://hdl.handle.net/10609/127057
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dc.contributor.authorOliva Navarro, Diego Alberto-
dc.contributor.authorCopado Mendez, Pedro Jesus-
dc.contributor.authorHinojosa, Salvador-
dc.contributor.authorPanadero Martínez, Javier-
dc.contributor.authorRiera Terrén, Daniel-
dc.contributor.authorJuan, Angel A.-
dc.contributor.otherUniversitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3)-
dc.contributor.otherUniversitat Oberta de Catalunya (UOC)-
dc.contributor.otherUniversidad de Guadalajara-
dc.contributor.otherInstituto Tecnológico y de Estudios Superiores de Monterrey-
dc.date.accessioned2021-01-26T10:01:18Z-
dc.date.available2021-01-26T10:01:18Z-
dc.date.issued2020-12-18-
dc.identifier.citationOliva, D. A., Copado, P. J., Hinojosa, S., Panadero, J., Riera, D., Juan, A. A.(2020). Fuzzy Simheuristics: Solving Optimization Problems under Stochastic and Uncertainty Scenarios. Mathematics, 8(12). pág. 1-19. doi: 10.3390/math8122240-
dc.identifier.issn2227-7390MIAR
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dc.identifier.urihttp://hdl.handle.net/10609/127057-
dc.description.abstractSimheuristics combine metaheuristics with simulation in order to solve the optimization problems with stochastic elements. This paper introduces the concept of fuzzy simheuristics, which extends the simheuristics approach by making use of fuzzy techniques, thus allowing us to tackle optimization problems under a more general scenario, which includes uncertainty elements of both stochastic and non-stochastic nature. After reviewing the related work, the paper discusses, in detail, how the optimization, simulation, and fuzzy components can be efficiently integrated. In order to illustrate the potential of fuzzy simheuristics, we consider the team orienteering problem (TOP) under an uncertainty scenario, and perform a series of computational experiments. The obtained results show that our proposed approach is not only able to generate competitive solutions for the deterministic version of the TOP, but, more importantly, it can effectively solve more realistic TOP versions, including stochastic and other uncertainty elements.en
dc.language.isoeng-
dc.publisherMathematics-
dc.relation.ispartofMathematics, 2020, 8(12)-
dc.relation.urihttps://doi.org/10.3390/math8122240-
dc.rightsCC BY-
dc.rights.urihttp://creativecommons.org/licenses/by/4.0-
dc.subjectsimulation-optimizationen
dc.subjectsimheuristicsen
dc.subjectfuzzy techniquesen
dc.subjectuncertaintyen
dc.subjectsimulación-optimizaciónes
dc.subjectsimheurísticaes
dc.subjecttécnicas difusases
dc.subjectincertidumbrees
dc.subjectsimulació-optimitzacióca
dc.subjectsimheurísticaca
dc.subjecttècniques difusesca
dc.subjectincertesaca
dc.subject.lcshAlgorithmsen
dc.titleFuzzy simheuristics: solving optimization problems under stochastic and uncertainty scenarios-
dc.typeinfo:eu-repo/semantics/article-
dc.subject.lemacAlgorismesca
dc.subject.lcshesAlgoritmoses
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess-
dc.identifier.doi10.3390/math8122240-
dc.gir.idAR/0000008454-
dc.relation.projectIDinfo:eu-repo/grantAgreement/PID2019-111100RB-C21-
dc.relation.projectIDinfo:eu-repo/grantAgreement/RED2018-102642-T-
dc.relation.projectIDinfo:eu-repo/grantAgreement/2019-I-ES01-KA103-062602-
dc.type.versioninfo:eu-repo/semantics/publishedVersion-
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