Sensitivity and uncertainty analysis in optimization programs using an evolutionary approach: A maintenance application

Indirect-Grouping Maintenance Strategy requires the calculation of an optimum (global) according to a minimization program P. However, the model on which the optimal is based may be incomplete in the sense that important uncertainties have not been considered. In order to evaluate the effects of the uncertainty of the parameters or how the uncertainty is propagated in the optimization program, the decision-maker needs to evaluate the range of variation of program P. In this work an innovative two step evolutionary approach to analyze uncertainties in Indirect-Grouping Maintenance Strategies is presented. The proposed approach combines the two proven techniques of Cellular Evolutionary Strategies and Evolutionary Strategies for the optimization problem. The approach does not guarantee the global optimum, but the experiments show that the results are very close to the real one. The examples presented confirm that the approach produces very good approximations for the range of the minimum when there is uncertainty in the model parameters and can be used as a tool for uncertainty/sensitivity analysis in other areas.