学术文献库

Mathematical models simulate various events under different conditions, enabling an early overview of the system to be implemented in practice, reducing the waste of resources and in less time. In project optimization, these models play a fundamental role, allowing to obtain parameters and attributes capable of enhancing product performance, reducing costs and operating time. These enhancements depend on several factors, including an accurate computational modeling of the inherent characteristics of the system. In general, such models include uncertainties in their mathematical formulations, which affect the feasibility of the results and their practical implementation. In this work, two different approaches capable of quantifying uncertainties during the optimization of mathematical models are considered. In the first, robust optimization, the sensitivity of decision variables in relation to deviations caused by external factors is evaluated. Robust solutions tend to reduce deviations due to possible system changes. The second approach, reliability-based optimization, measures the probability of system failure and obtains model parameters that ensures an established level of reliability. Overall, the fundamental objective is to formulate a multi-objective optimization problem capable of handling robust and reliability-based optimizations, to obtain solutions that are least sensitive to external noise and that satisfy prescribed reliability levels. The proposed formulation is analyzed by solving benchmark and chemical engineering problems. The results show the influence of both methodologies for the analysis of uncertainties, the multi-objective approach provides a variety of feasible optimizers, and the formulation proves to be flexible, so that the uncertainties can be incorporated into the problem considering the needs of each project.