Programação da produção em ambientes de manufatura aditiva: análise do estado-da-arte e proposta de método de resolução

• Ano de defesa: 2023
• Autor(a) principal: Dall'Agnol, Gabriela
• Orientador(a): Tavares Neto, Roberto Fernandes
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos; Câmpus São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Engenharia de Produção - PPGEP
• Palavras-chave em Português: Manufatura aditiva; Heurísticas; Meta-heurísticas; Modelagem matemática
• Palavras-chave em Inglês: Scheduling; Additive manufacturing; Heuristics; Meta-heuristics; Mathematical modeling
• Área do conhecimento CNPq: ENGENHARIAS::ENGENHARIA DE PRODUCAO::PESQUISA OPERACIONAL
• Link de acesso: https://repositorio.ufscar.br/handle/20.500.14289/18419

With the advancement of production technologies and materials, it is currently possible to use Additive Manufacturing (AM), also known as 3D printing, for the large-scale production of finished products, with numerous advantages, such as a high level of customization, simplification of factory floor and fast delivery. Production sequencing, known as Scheduling, is a well-established topic in his research area, but its application within an AM environment faces specific issues that have yet to be explored by researchers. The present study carries out a Systematic Literature Review (SLR) on the subject, in order to identify the main mathematical models, algorithms adopted for their solution and the main characteristics of the computational experiments carried out. The considered environment was mathematically modeled and, subsequently, a constructive heuristic solution multi-start based on the GRASP algorithm (Greedy Randomized Adaptive Search Procedure) was implemented in order to solve this problem. From generated instances, its performance was evaluated through computational experiments. The results showed that the proposed heuristic managed to obtain good results in very little computational time, in addition to achieving a low gap dispersion, indicating a high repeatability of the implemented heuristic.