A new multiple criteria data envelopment analysis with variable return to scale: Applying bi-dimensional representation and super-efficiency analysis

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorFederal University of Itajubá (UNIFEI)-
Autor(es): dc.creatorda Silva, Aneirson Francisco-
Autor(es): dc.creatorMiranda, Rafael de Carvalho-
Autor(es): dc.creatorMarins, Fernando Augusto Silva-
Autor(es): dc.creatorDias, Erica Ximenes-
Data de aceite: dc.date.accessioned2025-08-21T20:33:33Z-
Data de disponibilização: dc.date.available2025-08-21T20:33:33Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-04-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.ejor.2023.09.008-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/298954-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/298954-
Descrição: dc.descriptionIt is known that, in general, in practical real-world problems, when the number of Decision- Making Units (DMUs) is not large enough compared to the total number of input and output parameters, the traditional DEA models with Constant Return to Scale – CRS and with Variable Return to Scale – VRS have a weak power of discrimination, producing solutions that identify many DMUs as being efficient, in addition to obtaining unrealistic weight distributions. In this context, it is recommended to work with Multiple Criteria Data Envelopment Analysis - MCDEA models. So far, all MCDEA models available in the literature adopt CRS approach. This paper proposes a New Multiple Criteria Data Envelopment Analysis (NMCDEA) – VRS model, as well as performs a super-efficiency analysis for this model. Furthermore, through bi- dimensional graphic representations, a geometric demonstration is provided, showing that, in fact, the proposed model is a good representation of situations in which it is interesting to consider a VRS behavior. The results obtained through the optimization of instances available in the literature, for real instances, as well as the sensitivity analysis carried out, indicated that the NMCDEA-VRS has a much greater power of discrimination compared to the classic DEA–VRS model.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionSão Paulo State University (UNESP) Department of Production, Av. Dr. Ariberto Pereira da Cunha, 333-
Descrição: dc.descriptionFederal University of Itajubá (UNIFEI), Av. BPS, 1303-
Descrição: dc.descriptionSão Paulo State University (UNESP) Department of Production, Av. Dr. Ariberto Pereira da Cunha, 333-
Descrição: dc.descriptionCNPq: CNPq- 304197/2021-1-
Descrição: dc.descriptionCNPq: CNPq-303090/2021-9-
Descrição: dc.descriptionCNPq: CNPq-306868/2020-2-
Formato: dc.format308-322-
Idioma: dc.languageen-
Relação: dc.relationEuropean Journal of Operational Research-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectBi-dimensional representation-
Palavras-chave: dc.subjectData envelopment analysis-
Palavras-chave: dc.subjectManagerial decisions-
Palavras-chave: dc.subjectMultiple criteria data envelopment analysis-
Palavras-chave: dc.subjectVariable return to scale-
Título: dc.titleA new multiple criteria data envelopment analysis with variable return to scale: Applying bi-dimensional representation and super-efficiency analysis-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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