Multiple objectives and non-separability in stochastic dynamic programming

A general separable class of stochastic multiobjective optimization problems with perfect state information is considered. A generating approach using a stochastic multiobjective dynamic programming method is developed to find the set of non-inferior solutions. The results reveal the variation of the optimal weighting coefficient vector along a non-inferior trajectory. Non-separability is not an inherent property of dynamic programming. A general class of non-separable dynamic problems can be transformed into corresponding separable multiobjective dynamic programming problems. Multiobjective dynamic programming is shown to be a separation strategy to solve non-separable dynamic programming.

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