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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 fordoi:10.1504/ijrs.2012.044298 fatcat:ktkxmdemxnhtpayekpsfeb5fua