An efficient robustness analysis for the VEGA launch vehicle is essential to minimize the potential system failure during the ascending phase. Monte Carlo sampling method is usually considered as a reliable strategy in industry if the sampling size is large enough. However, due to a large number of uncertainties and a long response time for a single simulation, exploring the entire uncertainties sufficiently through Monte Carlo sampling method is impractical for VEGA launch vehicle. In order to make the robustness analysis more efficient when the number of simulation is limited, the quasi-Monte Carlo(Sobol, Faure, Halton sequence) and heuristic algorithm(Differential Evolution) are proposed. Nevertheless, the reasonable number of samples for simulation is still much smaller than the minimal number of samples for sufficient exploration. To further improve the efficiency of robustness analysis, the redundant uncertainties are sorted out by sensitivity analysis. Only the dominant uncertainties are remained in the robustness analysis. As all samples for simulation are discrete, many uncertainty spaces are not explored with respect to its objective function by sampling or optimization methods. To study these latent information, the meta-model trained by Gaussian Process is introduced. Based on the meta-model, the expected maximum objective value and expected sensitivity of each uncertainties can be analyzed for robustness analysis with much higher efficiency but without loss much accuracy.

This thesis focuses on considering of the investor economic risk during the preparation of the investment project including the financial plan, its evaluation and suggestions of proposals for risk reduction. Correct decisions on implementation or rejection of the project are based on the realistic financial plan. However, expenses and revenues are only implied and are burdened with some variability that leads to the risk of failure to achieve the planned values. To assess the acceptability of certain risks for the investor, an analysis of risk factors was conducted. The factors are first identified for their significance and potential negative impact, then the most risk factors are evaluated and determined whether it is necessary to further tracking or the risk to the investor is acceptable.