Nonlinear Stochastic Multiobjective Optimization Problem in Multivariate Stratified Sampling Design

Decision-making in survey sampling planning is a tricky situation; sometimes it involves multiple objectives, with various decision variables emanating from heterogeneous and homogeneous populations. Dealing the entire population under study its uncertain nature becomes challenging issue for researchers policymakers. Hence, an appropriate design optimization methodology are imperative. The presents useful discussion on stochastic multiobjective multivariate stratified (MSS) models theoretically, concepts illustrated numerical examples. Also, has been found that linearization of variance does not help determine optimal allocation problem minimum variability. Optimal problems weighted goal programming, Chebyshev programming methods also discussed Finally, linear approximation MSS conceptual theoretical framework environment. data simulated using stratifyR package.