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This work proposes a new method for approximating the Pareto front of a multi-objective simulation optimization problem (MOP) where the explicit forms of the objective functions are not available. The method iteratively approximates each objective function using a metamodeling scheme and employs a weighted sum method to convert the MOP into a set of single(More)
In this paper, we consider black-box problems where the analytic forms of the objective functions are not available, and the values can only be estimated by output responses from computationally expensive simulations. We apply the sample average approximation method to multi-objective stochastic optimization problems and prove the convergence properties of(More)
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