Journal of Statistics Applications & Probability


This paper introduces an efficient approach for stochastic vector optimization problem (SVOP) with random parameters in the right-hand side of the constraints. The proposed technique uses the scalarization concept to transform SVOP to a stochastic single objective optimization problem (SSOP) based on the nonnegative weighted sum approach. The statistical inference methods should be applied to convert SSOP into its equivalent deterministic single objective optimization problem (DSOP). The resulting problem can be solved as linear or nonlinear programming problem to obtain the efficient solutions. Finally, an illustrated example is given to verify the validity of the proposed approach.

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