Hybrid Genetic Algorithms Applied to the Glass Container Industry Problem

Amorim, Flaviana Moreira de Souza; Arantes, Márcio da Silva; Toledo, Cláudio Fabiano Motta; Frisch, Pierre Eric; Almada-Lobo, Bernardo

doi:10.1109/cec.2018.8477762

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…The application of deterministic mathematical models for problems in real production planning is widely used in literature [27][28][29][30]. These models assume exact knowledge of the system and perfect measurements.…”

Section: Fig 8: Flowchart Of Prediction and Decision Methodsmentioning

confidence: 99%

Stochastic Model For Setpoint of a Rolling Mill: An Application In The Soybean Oil Production

Ferreira

Arantes

et al. 2021

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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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Section: Fig 8: Flowchart Of Prediction and Decision Methodsmentioning

confidence: 99%

Stochastic Model For Setpoint of a Rolling Mill: An Application In The Soybean Oil Production

Ferreira

Arantes

et al. 2021

Preprint

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“…A figura 61 resume a relação entre as duas abordagens. A aplicação de modelos matemáticos determinísticos para problemas no planejamento real da produção é amplamente utilizada na literatura (TOLEDO et al, 2015;TOLEDO et al, 2016;Moreira de Souza Amorim et al, 2018;FACHINI;ESPOSTO;CAMARGO, 2017). Esses modelos assumem o conhecimento exato do sistema e medidas perfeitas.…”

Section: Modelo Estocástico Para Controle óTimo Do Laminadorunclassified

Modelagem Estocástica Aplicada a Problemas Industriais: Estudos de Casos Reais

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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.

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