Abstract
Este trabalho teve por objetivo avaliar a eficiência técnica dos cursos de Administração no Brasil, por meio da Análise Envoltória de Dados (DEA), uma técnica não paramétrica introduzida por Charnes, Cooper e Rhodes (1978). Foram utilizados como variáveis do modelo os oito indicadores que compõem o Conceito Preliminar de Cursos (CPC). A amostra foi composta de 1229 cursos de Administração, com base nos resultados do Exame Nacional de Desempenho dos Estudantes (ENADE) de 2009. Como resultado, constatou-se que apenas 1,2% destes cursos podem ser considerados eficientes. Após esta aferição, uma etapa de recomendações foi realizada a fim de propor algumas metas reparatórias aos cursos ineficientes, trazendo-se assim à tona a possibilidade de alcançarem melhores resultados, dados os insumos já disponíveis pelos cursos de Administração.

Abstract
In this paper, we explore the use of reference values (predictors) for the optimal objective function value of hard combinatorial optimization problems, instead of bounds, obtained by data mining techniques, and that may be used to assess the quality of heuristic solutions for the problem. With this purpose, we resort to the rectangular two-dimensional strip-packing problem (2D-SPP), which can be found in many industrial contexts. Mostly this problem is solved by heuristic methods, which provide good solutions. However, heuristic approaches do not guarantee optimality, and lower bounds are generally used to give information on the solution quality, in particular, the area lower bound. But this bound has a severe accuracy problem. Therefore, we propose a data mining-based framework capable of assessing the quality of heuristic solutions for the 2D-SPP. A regression model was fitted by comparing the strip height solutions obtained with the bottom-left-fill heuristic and 19 predictors provided by problem characteristics. Random forest was selected as the data mining technique with the best level of generalisation for the problem, and 30,000 problem instances were generated to represent different 2D-SPP variations found in real-world applications. Height predictions for new problem instances can be found in the regression model fitted. In the computational experimentation, we demonstrate that the data mining-based framework proposed is consistent, opening the doors for its application to finding predictions for other combinatorial optimisation problems, in particular, other cutting and packing problems. However, how to use a reference value instead of a bound, has still a large room for discussion and innovative ideas. Some directions for the use of reference values as a stopping criterion in search algorithms are also provided.