Uma proposta de aferição de índices baseado em DEA e uma aplicação ao caso das Universidades Federais Brasileiras

Detalhes bibliográficos
Ano de defesa: 2017
Autor(a) principal: Lima, Luciana Belo de
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal da Paraíba
Brasil
Engenharia de Produção
Programa de Pós-Graduação em Engenharia de Produção
UFPB
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: https://repositorio.ufpb.br/jspui/handle/tede/9941
Resumo: The objective of this research was to present an alternative form of efficiency indexes based on the realities presented by the agents under study. In this study, the agents studied were the Institutions of Higher Education (HEI) in the country. The methodology used was Data Envelopment Analysis (DEA), model Charnes, Cooper and Rhodes (CCR), with limits on the variables, which has as inputs the resources received by the institutions and as outputs all the products offered by these institutions. Given the measure obtained by this methodology, we investigated the adherence of this model to the current model applied by governmental institutions in the evaluation of these institutions. As a result, it was possible to observe that the federal universities are not considered compatible from the point of view of efficiency, because, according to the scenarios created, only 2 universities have achieved their efficiency, while the General Index of Courses (IGC), 11 universities were considered efficient. Thus, it was verified that the efficiency analysis model is considered to be applicable and significant, serving for the managers of the federal educational institutions to reassess whether the IGC quality indicator is considered efficient to evaluate the HEIs comprehensively, in order to maximize results while keeping resources constant over the long term.