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We develop a new method for approximating the Pareto front of a bi-objective stochastic optimization problem in which the expected objective functions are estimated by taking sample averaged outputs from expensive simulations. At each iteration of the proposed algorithm, a trust region is identified and quadratic approximate functions for the expected objective functions are built using the sample...
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