Estudo comparativo das aproximações baseadas no método de decomposição paramétrico para avaliar redes de filas de manufatura utilizando planejamento de experimentos

Abstract

This is a study of approximations based on parametric decomposition methods used in open queueing networks for modeling discrete job-shop manufacturing systems. These approximations play an important role in evaluating the performance of productive systems and have proved effective in many situations. Besides, these approximations are relatively easy to apply requiring fewer data compared to other methods because they use the average rate and SCV (square coefficient of variation) as the only parameters to characterize the network arrival and service processes. This work is aimed at analyzing and comparing several approximations since they are not yet available in the literature. Hence, several network situations were tested in order to identify the most adequate approximation for each situation. Firstly, a two-station network was analyzed followed by the analysis of a five-station network and lastly, a real example of a semiconductor plant, analyzed by Bitran e Tirupati (1988), was used. In order to reach these goals, the state of the art of approximation methods to evaluate the performance of open queueing networks was studied, and the approximations were compared using the experiment planning technique, important factors for building network configuration and data analysis The findings of this work demonstrate that approximations can be highly efficient to evaluate the performance of discrete job-shop manufacturing systems. Regardless of the configurations studied, it is worth mentioning that approximations 3 and 2, in general, showed the best results if compared to the other values obtained from simulations to evaluate the performance of open queueing networks, OQN,. The other approximations tended to overestimate E(Lj) when the number of stations is higher. This study intends to contribute to the development of computing systems in order to support project decisions and the planning and control of discrete manufacturing systems using approximations based on the parametric decomposition method