Seleção de fornecedores sob incertezas via otimização robusta

• Ano de defesa: 2019
• Autor(a) principal: Tavares, Cassiano da Silva
• Orientador(a): Munari Junior, Pedro Augusto
• 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: Seleção de fornecedores; Otimização sob incertezas; Otimização robusta
• Palavras-chave em Inglês: Supply selection; Uncertainty optimization; Robust optimization
• Área do conhecimento CNPq: ENGENHARIAS::ENGENHARIA DE PRODUCAO::PESQUISA OPERACIONAL
• Link de acesso: https://repositorio.ufscar.br/handle/20.500.14289/12419

This paper studies the problem of selection of suppliers under uncertainties, motivated by the current economic situation of world trade. The fierce search among the organizations for responsiveness in meeting the market demand has been directing efforts for optimization in the supply chain. One of the main links in this context is the supply of raw materials. The rupture of the supply of a raw material can cause the blockade or paralysis of the entire organizational system, leading to an operational failure to meet a demand, damaging the image of the organization to the market. The decision of the best choice of supply has become a vital activity for organizations in the current scenario, as the chain's operational performance is strongly tied to this fundamental link. With this, the decision to select suppliers becomes a very complex activity, requiring a high level of precision and assertiveness. The objective of this work is to develop and apply optimization approaches that incorporate the uncertainties in the context in which the global supply of raw materials is inserted through the Robust Optimization approach. Two models of mixed integer linear programming are proposed for the deterministic problem, from which the robust counterparts that model the problem under uncertainties are obtained. The models were implemented using general purpose optimization software. Monte Carlo simulations were performed to determine the performances of the deterministic and robust models in samples of different scenarios, as well as the level of robustness of the solutions. The computational experiments pointed out that the Robust Optimization approach enhances the robustness of solutions in risk aversion when uncertain parameters are involved. This was evidenced by the level of encumbrance promoted in the values of the solutions when a protection to the uncertainty was employed, since the increase in the optimal value of the objective function in the worst case is always smaller than the deviation of the uncertain parameters.