Discrete dynamic programming with outcomes in random variable structures
Multiobjective approach is the common way of generalization single-criterion dynamic programming models. Another way is to consider partially ordered criteria structures. That approach is rather rare. The aim of the paper is to present such a model. Generalization of Bellmans principle of optimality is employed to create a forward procedure to find the set of all maximal elements. As this set is usual large, the second problem under consideration is to find its subsets. To reduce the number of solutions presented to decision maker we propose to apply a family of narrowing relations. That approach is similar to scalarization in multiobjective programming. Ordered structures of random variables based on meanvariance, stochastic dominance and inverse stochastic dominance are considered. Numerical illustration is given at the end of the paper.
dynamic programming discrete optimization multiobjective decision making stochastic orders