A new algorithm to find the most favourable constituency using data envelopment analysis
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://doi.org/10.34627/rcc.v2i0.61 |
Resumo: | DEA is a mathematical programming technique presented in 1978 by Charnes, Cooper and Rhodes, which focused mainly on the efficiency assessment of not-for-profit organizations. When constructing a DEA model, a major decision is the choice of inputs and outputs for the study. The CCR DEA model is not suited for studies with constituencies with dissonant judgments about the desirability of the attributes. This problem is overcome by the work of Bougnol and Dula, in which a new model is introduced but with long processing times. A faster new formulation is presented by means of a Mixed Binary Linear Programming Model. Tests concerning the computational advantages of this formulation were carried out on multivariate random normal generated by the Distribution View Software from J. Coelho. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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A new algorithm to find the most favourable constituency using data envelopment analysisUm Novo Algoritmo para Encontrar a Constituência Mais Favorável na Análise de Dados pela EnvolventeDEA is a mathematical programming technique presented in 1978 by Charnes, Cooper and Rhodes, which focused mainly on the efficiency assessment of not-for-profit organizations. When constructing a DEA model, a major decision is the choice of inputs and outputs for the study. The CCR DEA model is not suited for studies with constituencies with dissonant judgments about the desirability of the attributes. This problem is overcome by the work of Bougnol and Dula, in which a new model is introduced but with long processing times. A faster new formulation is presented by means of a Mixed Binary Linear Programming Model. Tests concerning the computational advantages of this formulation were carried out on multivariate random normal generated by the Distribution View Software from J. Coelho.DEA é uma técnica de programação matemática apresentada em 1978 por Charnes, Cooper e Rhodes, focado principalmente na avaliação da eficiência de organizações com finalidades não-lucrativas. Ao construir um modelo de DEA, uma decisão principal é a escolha dos “inputs” e dos “outputs” para o estudo. O modelo de DEA não é adequado para estudos com julgamentos díspares sobre a preferência dos atributos. Isto é superado pelo trabalho de Bougnol e de Dula onde um modelo novo é introduzido mas com tempos de processamento muito elevados. Um algoritmo novo mais rápido é apresentado por meio de um modelo de programação linear binário misto resolvido pelo algoritmo de corte e ramificação. Os testes das vantagens computacionais desta formulação nova foram executados em dados multivariados normais gerados pelo programa “Distribution View” de J. Coelho.Universidade Aberta2018-04-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/otherinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://doi.org/10.34627/rcc.v2i0.61oai:ojs2.journals.uab.pt:article/61Revista de Ciências da Computação; v. 2 (2007); 56-642182-18011646-633010.34627/rcc.v2i0reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAPporhttps://journals.uab.pt/index.php/rcc/article/view/61https://doi.org/10.34627/rcc.v2i0.61https://journals.uab.pt/index.php/rcc/article/view/61/95Direitos de Autor (c) 2018 Universidade Abertahttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessSantos, Jorge M. A.2022-10-25T11:31:52Zoai:ojs2.journals.uab.pt:article/61Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:13:58.908238Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A new algorithm to find the most favourable constituency using data envelopment analysis Um Novo Algoritmo para Encontrar a Constituência Mais Favorável na Análise de Dados pela Envolvente |
title |
A new algorithm to find the most favourable constituency using data envelopment analysis |
spellingShingle |
A new algorithm to find the most favourable constituency using data envelopment analysis Santos, Jorge M. A. |
title_short |
A new algorithm to find the most favourable constituency using data envelopment analysis |
title_full |
A new algorithm to find the most favourable constituency using data envelopment analysis |
title_fullStr |
A new algorithm to find the most favourable constituency using data envelopment analysis |
title_full_unstemmed |
A new algorithm to find the most favourable constituency using data envelopment analysis |
title_sort |
A new algorithm to find the most favourable constituency using data envelopment analysis |
author |
Santos, Jorge M. A. |
author_facet |
Santos, Jorge M. A. |
author_role |
author |
dc.contributor.author.fl_str_mv |
Santos, Jorge M. A. |
description |
DEA is a mathematical programming technique presented in 1978 by Charnes, Cooper and Rhodes, which focused mainly on the efficiency assessment of not-for-profit organizations. When constructing a DEA model, a major decision is the choice of inputs and outputs for the study. The CCR DEA model is not suited for studies with constituencies with dissonant judgments about the desirability of the attributes. This problem is overcome by the work of Bougnol and Dula, in which a new model is introduced but with long processing times. A faster new formulation is presented by means of a Mixed Binary Linear Programming Model. Tests concerning the computational advantages of this formulation were carried out on multivariate random normal generated by the Distribution View Software from J. Coelho. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-04-02 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/other |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://doi.org/10.34627/rcc.v2i0.61 oai:ojs2.journals.uab.pt:article/61 |
url |
https://doi.org/10.34627/rcc.v2i0.61 |
identifier_str_mv |
oai:ojs2.journals.uab.pt:article/61 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://journals.uab.pt/index.php/rcc/article/view/61 https://doi.org/10.34627/rcc.v2i0.61 https://journals.uab.pt/index.php/rcc/article/view/61/95 |
dc.rights.driver.fl_str_mv |
Direitos de Autor (c) 2018 Universidade Aberta http://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Direitos de Autor (c) 2018 Universidade Aberta http://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Aberta |
publisher.none.fl_str_mv |
Universidade Aberta |
dc.source.none.fl_str_mv |
Revista de Ciências da Computação; v. 2 (2007); 56-64 2182-1801 1646-6330 10.34627/rcc.v2i0 reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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