Please use this identifier to cite or link to this item: https://hdl.handle.net/11000/6259
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dc.contributor.authorGonzález, Martín-
dc.contributor.authorLópez Espín, José Juan-
dc.contributor.authorAparicio Baeza, Juan-
dc.contributor.authorGiménez, Domingo-
dc.contributor.authorTalbi, El-Ghazali-
dc.contributor.otherDepartamentos de la UMH::Estadística, Matemáticas e Informáticaes
dc.date.accessioned2020-09-01T10:29:29Z-
dc.date.available2020-09-01T10:29:29Z-
dc.date.created2017-06-08-
dc.date.issued2020-09-01-
dc.identifier.urihttp://hdl.handle.net/11000/6259-
dc.description.abstractData Envelopment Analysis (DEA) is a nonparametric methodology for estimating technical efficiency of a set of Decision Making Units (DMUs) from a dataset of inputs and outputs. This paper is devoted to computational aspects of DEA models under the application of the Principle of Least Action. This principle guarantees that the efficient closest targets are determined as benchmarks for each assessed unit. Usually, these models have been addressed in the literature by applying unsatisfactory techniques, based fundamentally on combinatorial NPhard problems. Recently, some heuristics have been developed to partially solve these DEA models. This paper improves the heuristic methods used in previous works by applying a combination of metaheuristics and an exact method. Also, a parameterized scheme of metaheuristics is developed in order to implement metaheuristics and hybridations/combinations, adapting them to the particular problem proposed here. In this scheme, some parameters are used to study several types of metaheuristics, like Greedy Random Adaptative Search Procedure, Genetic Algorithms or Scatter Search. The exact method is included inside the metaheuristic to solve the particular model presented in this paper. A hyperheuristic is used on top of the parameterized scheme in order to search, in the space of metaheuristics, for metaheuristics that provide solutions close to the optimum. The method is competitive with exact methods, obtaining fitness close to the optimum with low computational timees
dc.description.sponsorshipJ. Aparicio and M. González thank the financial support from the Spanish ‘Ministerio de Economa, Industria y Competitividad’ (MINECO), the ‘Agencia Estatal de Investigacion’ and the ‘Fondo Europeo de Desarrollo Regional’ under grant MTM2016-79765-P (AEI/FEDER, UE).-
dc.description.sponsorshipAdditionally, D. Giméenez thanks the financial support from the Spanish MINECO, as well as by European Commission FEDER funds, under grant TIN2015-66972-C5-3-R.-
dc.formatapplication/pdfes
dc.format.extent8es
dc.language.isoenges
dc.rightsinfo:eu-repo/semantics/openAccesses
dc.subjectMathematical modeles
dc.subjectComputational modelinges
dc.subjectData envelopment analysises
dc.subjectMathematical programminges
dc.subjectOperations researches
dc.subjectGenetic algorithmses
dc.subjectProductiones
dc.subject.other517 - Análisises
dc.titleA parameterized scheme of metaheuristics with exact methods for determining the Principle of Least Action in Data Envelopment Analysises
dc.typeinfo:eu-repo/semantics/articlees
dc.identifier.doi10.1109/CEC.2017.7969364-
dc.relation.publisherversionhttps://doi.org/10.1109/CEC.2017.7969364-
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