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Sistema de apoio à decisão para programação da produção em fundições de mercado.
(Universidade Federal de São Carlos, 2005-01-27) Teixeira Junior, Rodolfo Florence; Pereira, Neocles Alves; https://lattes.cnpq.br/3744216524256924; Fernandes, Flavio Cesar Faria
The aim of production scheduling is to time the use of resources to carry out the production orders required to meet a previously defined production need. Due to the changes in the production settings, defined by the increase in competition and diversity of products, there has been increasing complexity in the development of production scheduling process, mainly when complex production settings are considered. Despite the developments in the area, a significant number of production settings still do not use the applicable solutions in the area. The initial aims of this work were to create alternatives to fill this gap. From these findings and based on an initial survey, carried out in the interior of São Paulo State, regarding the productive settings of foundries which work with customized products made to order, also known as steel casting foundries, effective alternatives for the production scheduling process for this specific production settings were sought. This work presents a viable proposal for the production scheduling process in steel casting foundries, considering its main particularities and restrictions, using a decision support system, which employs a single cycle system known as Period Batch Control (PBC). Among the results obtained, a methodology for the rationalization and systematization of the decision-making process in the production control was proposed. For the development of a decision support system in ordering process, the following viable decision-making models were proposed: a model of binary integer programming; a model based on classical approximation methods, specifically the method known as beam search ; and a meta-heuristic model based on genetic algorithms. The results obtained from the application of decision-making models in experimental production situations similar to the reality of production in steel casting foundries have confirmed the practical viability of these models, providing a comparison of the results between distinct modeling techniques. It has also contributed to the designing of guidelines for other applications of decision support systems in the operational area of complex production settings. Finally, the suitable of the proposed system was conformed by a later survey carried out in the main steel casting foundries in the interior of São Paulo State.
Reamostragem bootstrap em amostragem por conjuntos ordenados e intervalos de confiança não paramétricos para a média
(Universidade Federal de São Carlos, 2005-01-27) Taconeli, Cesar Augusto; Barreto, Maria Cecília Mendes; https://lattes.cnpq.br/2218053283392257
Ranked set sampling is an efficient and practice way to obtain more precise estimative when the sample size is small because of the high cost or difficulties to measure the interest variable. Using rough and cheap qualitative or quantitative information, the sample units are ranked before their effective measurement. In 1952, McIntyre introduced the ranked set sample design to estimate the average yields from plots of cropland, using the ranked set sample mean, X . Cesario and Barreto (2003) have shown a parametric version of bootstrap confidence intervals for normal distribution mean. Because of the restriction of small sample size, the distributional assumption may not be reasonable, producing no liable estimates. So the study and proposition of precise interval estimators of the population mean could be relevant and are the main interest of this work. Using resampling methods, we propose in this work an extension of bootstrap resampling for ranked set sampling. A simulation study is conduced to the properties of single random sample bootstrap confidence intervals and the similar using our version for ranked set sampling. The analysis of the simulation study have shown the gain of precision for using the ranked set sampling bootstrap confidence intervals in the population mean.