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dc.creatorJuan, Ángel Alejandroes_ES
dc.creatorFaulín Fajardo, Javieres_ES
dc.creatorGrasman, Scott Erwines_ES
dc.creatorRabe, Markuses_ES
dc.creatorFigueira, Gonçaloes_ES
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.description.sponsorshipThis work has been partially supported by the Spanish Ministry of Economy and Competitiveness (grant TRA2013-48180-C3-P), FEDER, and the Ibero-American Programme for Science and Technology for Development (CYTED2014-515RT0489). Likewise we want to acknowledge the support received by the Department of Universities, Research & Information Society of the Catalan Government (Grant 2014-CTP-00001) and the CAN Foundation (Navarre, Spain) (Grant 3CAN2014-3758).en
dc.publisherElsevier Ltd.en
dc.relation.ispartofOperations Research Perpsectives, 2 (2015) 62-72en
dc.rights© 2015 The Authors. This is an open access article under the CC BY licenseen
dc.subjectCombinatorial optimizationen
dc.subjectStochastic problemsen
dc.titleA review of simheuristics: extending metaheuristics to deal with stochastic combinatorial optimization problemsen
dc.typeArtículo / Artikuluaes
dc.contributor.departmentUniversidad Pública de Navarra. Departamento de Estadística e Investigación Operativaes_ES
dc.contributor.departmentNafarroako Unibertsitate Publikoa. Estatistika eta Ikerketa Operatiboa Sailaeu
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.type.versionVersión publicada / Argitaratu den bertsioaes

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© 2015 The Authors. This is an open access article under the CC BY license
Except where otherwise noted, this item's license is described as © 2015 The Authors. This is an open access article under the CC BY license