Simulated polyhedral clouds in robust optimisation

Past studies of uncertainty handling with polyhedral clouds have already shown strength in dealing with higher dimensional uncertainties in robust optimisation, even in case of partial ignorance of statistical information. However, the number of function evaluations necessary to quantify and propagate the uncertainties has been too high to be useful in many real-life applications with respect to limitations of computational cost. In this paper, we propose a simulation-based approach for optimisation over a polyhedron, inspired by the Cauchy deviates method. Thus, we achieve a computationally efficient method to compute worst-case scenarios with polyhedral clouds which we embed in a robust optimisation problem formulation. We apply the method to two test cases from space system design.