Em primeiro lugar a Deus por me permitir essa oportunidade, vencendo qualquer medo e obstáculo pelo caminho. A minha avo Ana, que ja nao se encontra entre nás, mas sempre me apoiou em todos os momentos que esteve presente, a minha mae Lálian, pois sempre me deu forcas e confianca para poder conseguir atingir minhas metas. Aos meus primos Jordão, Vasconcelos e Fabiane, que sao tambem meus irmaos, por terem me acolhido em toda essa jornada, e tambem pela força, confianca e compreensao. Ao meu namorado, Danilo, pelo apoio que tem me dado todo esse tempo, pelo seu amor e por sempre me acalmar nas horas de desespero. Sem duvida alguma, agradeco de todo coracao ao professores que me acompanharam em toda minha caminhada, desde a graduacão ate a finalizacao de meu mestrado. V As minhas amigas incondicionais Iasmim, Liliane, Larissa, Lálian Carla, Mirelle e Mirielle que mesmo estando distantes sempre mantemos contato, nos preocupando de verdade umas com as outras, pois a distancia nao importa quando temos verdadeiras amizades. V .A toda minha família, a todos os meus tios, tias e primos por sempre estarem do meu lado quando preciso e sempre acreditar em mim. A minha orientadora Dra. Rosana Sueli da Motta Jafelice, por sua dedicaçao e compro metimento, pela paciencia, por estar sempre a disposicão quando preciso de ajuda e por seus conhecimentos transmitidos durante este período de mestrado. Aos professores Dra. Ana Maria Amarillo Bertone, Dr. Cesar Guilherme de Almeida e Dr. Marcos Antonio da Camara pelos conhecimentos compartilhados durante os Seminarios de Matemíatica Aplicada. Ao professor Dr. Leonardo Sanches que me ajudou muito em todo desenvolvimento deste trabalho, que o considero meu coorientador. Aos meus amigos do mestrado que fizeram parte da minha vida durante essa etapa, e que foram sem duvidas muito importante pra mim:
This doctoral thesis aims to present real case studies developed to address industrial problems. Taking into consideration that in real factory environments the production process may suffer variations from several sources of uncertainty, the focus of the study is to build models considering such variations. To address the problems efficiently, we first propose the use of a new technique: Chance-constraint with Risk Allocation(CCRA). In addition to the CCRA technique, we address the stochastic two-stage technique, widely used in the literature to solve production planning problems with stochasticity. Both techniques are compared and their advantages are reported. The first case study represents the reality of an Industry of Production of Pots and Ampoules (IPPA) located in Minas Gerais. The main decision to be made consists in dimensioning the quantity of ampoules and pots that must be produced over a time horizon, in order to meet a certain demand. To solve the problem it was applied the exact method Branch-and-Cut (B&C), a simple Genetic Algorithm (GA) and a Multi-population Genetic Algorithm (MPGA). The results showed that the metaheuristics are able to find the optimal solutions obtained by the exact B&C method. In groups of more complex instances, the metaheuristics outperform the B&C method. In this context, the Production of Pots and Ampoules tool was also developed, to assist in the development of optimized production schedules. In the second case study we are dealing with a production process in an oil extraction industry, where it is necessary to determine the optimal settings in order to obtain the highest efficiency in soybean oil extraction. The entire extraction process is complex, so we will deal with only one main piece of equipment, the rolling mill, whose function is to transform the broken soybeans into small flakes. This equipment is manually controlled by applying hydraulic pressure to the rolls. With the objective of improving production efficiency, aiming to obtain pressure adjustments and flake measurements in an intelligent and automatic way, this paper proposes a stochastic mathematical model that aims to obtain optimal pressure setpoints for a rolling mill, in order to maintain the flake thicknesses in the ideal operation pattern. The results obtained have proven to be superior to the measures that are currently adopted in the factory, increasing annual profits.
A scientific challenge on industrial production is to mathematically represent a production process, and this challenge increases when describing production processes with stochastic behavior. The present paper will be approaching a specific part of the production process of soybean oil, where the main objective is to maximize the oil extraction by keeping the thicknesses of the soybean flakes within an operating range. We propose a method, based on a mathematical stochastic model, to obtain pressure setpoints that produce flakes as ideal as possible for oil extraction. The results reported are achieved by applying the proposed method in the industry with improvements within the process in terms of time and quality.
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