Please use this identifier to cite or link to this item: http://hdl.handle.net/10609/92802
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dc.contributor.authorJuan Pérez, Ángel Alejandro-
dc.contributor.authorFaulin Fajardo, Francisco Javier-
dc.contributor.authorGrasman, Scott Erwin-
dc.contributor.authorRabe, Markus-
dc.contributor.authorFigueira, Gonçalo-
dc.contributor.otherUniversidad Pública de Navarra-
dc.contributor.otherUniversidade do Porto-
dc.contributor.otherUniversity of Dortmund-
dc.contributor.otherRochester Institute of Technology-
dc.contributor.otherUniversitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3)-
dc.date.accessioned2019-04-02T13:44:37Z-
dc.date.available2019-04-02T13:44:37Z-
dc.date.issued2015-12-01-
dc.identifier.citationJuan, A.A., Faulín, F., Grasman, S., Rabe, M. & Figueira, G. (2015). A review of simheuristics: extending metaheuristics to deal with stochastic combinatorial optimization problems. Operations Research Perspectives, 2(), 62-72. doi: 10.1016/j.orp.2015.03.001-
dc.identifier.issn2214-7160MIAR
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dc.identifier.urihttp://hdl.handle.net/10609/92802-
dc.description.abstractMany combinatorial optimization problems (COPs) encountered in real-world logistics, transportation, production, healthcare, financial, telecommunication, and computing applications are NP-hard in nature. These real-life COPs are frequently characterized by their large-scale sizes and the need for obtaining high-quality solutions in short computing times, thus requiring the use of metaheuristic algorithms. Metaheuristics benefit from different random-search and parallelization paradigms, but they frequently assume that the problem inputs, the underlying objective function, and the set of optimization constraints are deterministic. However, uncertainty is all around us, which often makes deterministic models oversimplified versions of real-life systems. After completing an extensive review of related work, this paper describes a general methodology that allows for extending metaheuristics through simulation to solve stochastic COPs. 'Simheuristics' allow modelers for dealing with real-life uncertainty in a natural way by integrating simulation (in any of its variants) into a metaheuristic-driven framework. These optimization-driven algorithms rely on the fact that efficient metaheuristics already exist for the deterministic version of the corresponding COP. Simheuristics also facilitate the introduction of risk and/or reliability analysis criteria during the assessment of alternative high-quality solutions to stochastic COPs. Several examples of applications in different fields illustrate the potential of the proposed methodology.en
dc.language.isoeng-
dc.publisherOperations Research Perspectives-
dc.relation.ispartofOperations Research Perspectives, 2015, 2()-
dc.relation.urihttps://doi.org/10.1016/j.orp.2015.03.001-
dc.rightsCC BY-
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/-
dc.subjectmetaheuristicsen
dc.subjectsimulationen
dc.subjectcombinatorial optimizationen
dc.subjectstochastic problemsen
dc.subjectmetaheurístiquesca
dc.subjectsimulacióca
dc.subjectoptimització combinatòriaca
dc.subjectproblemes estocàsticsca
dc.subjectmetaheurísticaes
dc.subjectsimulaciónes
dc.subjectoptimización combinatoriaes
dc.subjectproblemas estocásticoses
dc.subject.lcshHeuristicen
dc.titleA review of simheuristics: extending metaheuristics to deal with stochastic combinatorial optimization problems-
dc.typeinfo:eu-repo/semantics/review-
dc.typeinfo:eu-repo/semantics/publishedVersion-
dc.subject.lemacHeurísticaca
dc.subject.lcshesHeurísticaes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess-
dc.identifier.doi10.1016/j.orp.2015.03.001-
dc.gir.idAR/0000004424-
dc.relation.projectIDinfo:eu-repo/grantAgreement/TRA2013-48180-C3-P-
dc.relation.projectIDinfo:eu-repo/grantAgreement/CYTED2014-515RT0489-
dc.relation.projectIDinfo:eu-repo/grantAgreement/2014-CTP-00001-
dc.relation.projectIDinfo:eu-repo/grantAgreement/3CAN2014-3758-
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