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Applying global sensitivity analysis to solutions obtained from optimization gives an understanding of which variables have most impact on the solution. It also provides confidence in the optimality of the solution(s). Typically, global sensitivity analysis is based on a linear regression model in the stepwise manner. To improve computational efficiency, solutions obtained from optimization can be re-used to compute global sensitivities of variables. This paper investigates the extent to which the procedure options of stepwise regression analysis can influence the rank-order of variables importance, when using solutions taken from optimization. It is concluded that the stepwise regression analysis applied to ranktransformed data from the first 100 optimization solutions, through bidirectional elimination and BIC, can rank the most important variables fast and accurately. In contrast, the production of more detailed information requires the use of AIC and larger sample sizes.
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