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

Detalhes bibliográficos
Ano de defesa: 2023
Autor(a) principal: Dall'Agnol, Gabriela
Orientador(a): Tavares Neto, Roberto Fernandes lattes
Banca de defesa: Não Informado pela instituição
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
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Palavras-chave em Inglês:
Área do conhecimento CNPq:
Link de acesso: https://repositorio.ufscar.br/handle/20.500.14289/18419
Resumo: 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.