Teses

URI permanente para esta coleçãohttps://rigalileo.itegam.org.br/handle/123456789/175

Teses produzidas oriundas de convênios com universidades

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2020 - 20232

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Data inicial: 2020Data final: 2023

Resultados da Pesquisa

Agora exibindo 1 - 2 de 2
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    Modelo de Reprogramação de Produção em Flow Shop Híbrido Unidirecional Integrando Fabricante por Contrato e seus Clientes
    (Universidade Federal de Santa Catarina, 2020) UHLMANN, Iracyanne Retto; FRAZZON, Enzo Morosini
    Contract Manufacturers (CMs) usually schedule their production considering the requirements and constraints of each purchasing industry separately. This strategy limits the quality of production rescheduling due to difficulties in evaluating capacity utilization opportunities and the need to renegotiate delivery commitments. This research aims to propose a novel unidirectional hybrid flow shop rescheduling model to address the integration of a contract manufacturer, responsible for production execution and inventory control, and its industrial customers, who manage delivery planning. The study was structured in three phases: identifying research gaps and opportunities, developing the conceptual model, and modeling a real hybrid flow shop using a multi-method approach. The results highlight the model’s potential to improve production execution and delivery performance, enhance partnerships among stakeholders, and reduce the need for renegotiation.
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    USO DE TÉCNICAS DE OTIMIZAÇÃO METAHEURISTICAS APLICADAS AO DESPACHO ECONÔMICO E AMBIENTAL DE USINAS TERMOELÉTRICAS E FOTOVOLTAICAS
    (Universidade Federal do Pará, 2023) Santos, Eliton Smith dos; NUNES, Marcus Vinicius Alves
    Economic Load Dispatch (ELD) aims at planning and operating Thermoelectric Power Plants (TPP) to meet energy demand at the lowest cost. However, traditional ELD does not consider environmental costs, which are crucial under current sustainability concerns. Thus, this thesis proposes the use of metaheuristic techniques — Ant Lion Optimizer (ALO), Dragonfly Algorithm (DA), and Differential Evolution (DE) — to optimize the Environmental Economic Dispatch (EED), integrating photovoltaic solar generation. The proposal includes an intelligent algorithm that adjusts TPP motor power based on demand, allowing the shutdown of less efficient motors and reducing emissions. Simulations performed in the MATLAB environment, with a hybrid model of six Generating Units (GU) and thirteen Solar Photovoltaic Plants (SPP), demonstrated that the DE technique yielded the best results, reducing fossil fuel consumption by 3.02% and atmospheric emissions by 1.42%.