Increasing the efficiency in integer simulation optimization: Reducing the search space through data envelopment analysis and orthogonal arrays

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dc.contributor.authorMiranda, Rafael de Carvalho
dc.contributor.authorMontevechi, José Arnaldo Barra
dc.contributor.authorda Silva, Aneirson Francisco [UNESP]
dc.contributor.authorMarins, Fernando Augusto Silva [UNESP]
dc.contributor.institutionAvenida BPS
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2018-12-11T17:32:14Z
dc.date.available2018-12-11T17:32:14Z
dc.date.issued2017-10-16
dc.description.abstractThe development of various heuristics has enabled optimization in simulation environments. Nevertheless, this research area remains underexplored, primarily with respect to the time required for convergence of these heuristics. In this sense, simulation optimization is influenced by the complexity of the simulation model, the number of variables, and by their ranges of variation. Within this context, this paper proposes a method capable of identifying the best ranges for each integer decision variable within the simulation optimization problem, thereby providing a reduction in computational cost without loss of the quality in the response. The proposed method combines experimental design techniques, Discrete Event Simulation, and Data Envelopment Analysis. The experimental designs called orthogonal arrays are used to generate the input scenarios to be simulated, and super-efficiency analysis is applied in a Data Envelopment Analysis model with variable returns to scale to rank the input scenarios. The use of the super-efficiency concept enables to distinguish the most efficient input scenarios, which allows for the ranking of all the orthogonal array scenarios used. The values of the variables of the two input scenarios that present the highest values of super-efficiency are adopted as the new range of the optimization problem. To illustrate this method's use and advantages, it was applied to real cases associated with integer simulation optimization problems. Based on the results, the effectiveness of this approach is verified because it delivered considerable reductions in the search space and in the computational time required to obtain a solution without affecting the quality.en
dc.description.affiliationFederal University of Itajubá (UNIFEI) Avenida BPS, 1303 – Caixa Postal: 50 – Itajubá
dc.description.affiliationSao Paulo State University (UNESP). Av. Dr. Ariberto Pereira da Cunhsa, 333, Guaratinguetá
dc.description.affiliationUnespSao Paulo State University (UNESP). Av. Dr. Ariberto Pereira da Cunhsa, 333, Guaratinguetá
dc.format.extent673-681
dc.identifierhttp://dx.doi.org/10.1016/j.ejor.2017.04.016
dc.identifier.citationEuropean Journal of Operational Research, v. 262, n. 2, p. 673-681, 2017.
dc.identifier.doi10.1016/j.ejor.2017.04.016
dc.identifier.file2-s2.0-85018264755.pdf
dc.identifier.issn0377-2217
dc.identifier.scopus2-s2.0-85018264755
dc.identifier.urihttp://hdl.handle.net/11449/178818
dc.language.isoeng
dc.relation.ispartofEuropean Journal of Operational Research
dc.relation.ispartofsjr2,437
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectData envelopment analysis
dc.subjectInteger simulation optimization
dc.subjectSimulation
dc.subjectSuper-efficiency
dc.titleIncreasing the efficiency in integer simulation optimization: Reducing the search space through data envelopment analysis and orthogonal arraysen
dc.typeArtigo
unesp.author.lattes2151242493491034[3]
unesp.author.lattes9008186664173955[4]
unesp.author.orcid0000-0001-9170-8626[1]
unesp.author.orcid0000-0001-6510-9187[4]
unesp.author.orcid0000-0002-2215-0734[3]

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