Métrica de robustez com base em desempenho, complexidade e estabilidade de sistema

• Ano de defesa: 2022
• Autor(a) principal: Paiva, João Ricardo Braga de
• Orientador(a): Calixto, Wesley Pacheco
• Banca de defesa: Calixto, Wesley Pacheco, Reis, Márcio Rodrigues da Cunha, Silva, Alan Henrique Ferreira, Gomes, Viviane Margarida, Cruz Junior, Gelson da
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Goiás
• Programa de Pós-Graduação: Programa de Pós-graduação em Engenharia Elétrica e da Computação (EMC)
• Departamento: Escola de Engenharia Elétrica, Mecânica e de Computação - EMC (RMG)
• País: Brasil
• Palavras-chave em Português: Robustez; Desempenho; Complexidade; Estabilidade; Métrica
• Palavras-chave em Inglês: Robustness; Performance; Complexity; Stability; Metric
• Área do conhecimento CNPq: ENGENHARIAS
• Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/12616

This work proposes a metric that evaluates the system's robustness based on performance, complexity, and stability. The methodology considers the system's internal dynamics and interactions with the external environment using complexity measure, also evaluates the system's ability in returning to equilibrium after an adverse situation. The proposed robustness metric application makes it possible comparisons between different resource configurations for the system under analysis. The proposed robustness metric application allows comparison between distinct resource configurations for the subject system. Three models are used for case studies: i) intensive care unit system model, ii) process scheduling system model, and iii) electric vehicle traction and braking system model. It is identified that in the scenarios with greater robustness than that recorded for the base case, the component's contribution to the metric is approximate, with performance contribution at around 30% and the complexity and stability contributions at about 35%. In scenarios with less robustness than that recorded for the base case, there is a greater variation degree in components' contribution to the robustness measure. Results indicate that the proposed metric can be used as a tool for quantitative analyses, which only consider robustness, and qualitative analyses, which consider performance, complexity, and stability together with robustness. Applying the proposed metric increases information level about the system, allowing better-reasoned decision.