Avaliação de economias de escala e escopo no desempenho de equipes de serviços aplicando Análise de Envoltória de Dados DEA: um estudo em Empresa de Telecomunicações no Brasil

This Master s Thesis proposes the application of Data Envelopment Analysis DEA to evaluate economies of scale and economies of scope in the performance of service teams involved with installation of data communication circuits, based on the study of a major telecommunication company in Brazil. Data was collected from the company s Operational Performance Division. Initial analysis of a data set, including nineteen installation teams, was performed considering input oriented methods. Subsequently, the need for restrictions on weights is analyzed using the Assurance Region method, checking for the existence of zero-valued weights. The resulting returns to scale are then verified. Further analyses using the Assurance Region Constant (AR-I-C) and Variable (AR-I-V) models verify the existence of variable, rather than constant, returns to scale. Therefore, all of the final comparisons use scores obtained through the AR-I-V model. In sequence, we verify if the system has economies of scope by analyzing the behavior of the scores in terms of individual or multiple outputs. Finally, conventional results, used by the company in study to evaluate team performance, are compared to those generated using the DEA methodology. The results presented here show that DEA is a useful methodology for assessing team performance and that it may contribute to improvements on the quality of the goal setting procedure.

Técnicas de inteligência artificial para a geração dinâmica de set points para uma coluna de destilação

Artificial Intelligence techniques are applied to improve performance of a simulated oil distillation system. The chosen system was a debutanizer column. At this process, the feed, which comes to the column, is segmented by heating. The lightest components become steams, by forming the LPG (Liquefied Petroleum Gas). The others components, C5+, continue liquid. In the composition of the LPG, ideally, we have only propane and butanes, but, in practice, there are contaminants, for example, pentanes. The objective of this work is to control pentane amount in LPG, by means of intelligent set points (SP s) determination for PID controllers that are present in original instrumentation (regulatory control) of the column. A fuzzy system will be responsible for adjusting the SP's, driven by the comparison between the molar fraction of the pentane present in the output of the plant (LPG) and the desired amount. However, the molar fraction of pentane is difficult to measure on-line, due to constraints such as: long intervals of measurement, high reliability and low cost. Therefore, an inference system was used, based on a multilayer neural network, to infer the pentane molar fraction through secondary variables of the column. Finally, the results shown that the proposed control system were able to control the value of pentane molar fraction under different operational situations