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Deterministic long run average problems of optimal control are ''asymptotically equivalent" to infinite-dimensional linear programming problems ( LPP ) and the latter are approximated by finite dimensional LPP. The solutions of this finite dimensional LPP can be used for numerical analysis of periodic optimization problems. In the present paper we establish the convergence of controls constructed on the basis of the solution of the finite dimensional LPP to the optimal control of a periodic optimization problem. Results are illustrated with a numerical example.

The purpose of this paper is two fold. First to establish the Theory of discounted constrained Markov Decision Process with a countable state and action spaces with general multi-chain structure. Second, to introduce finite approximation methods. We define the occupation measures and obtain properties of the set of all achievable occupation measures under the different admissible policies. We establish the optimality of stationary policies for the constrained control problem, and a LP with a countable number of decision variables through wich optimal stationary policies are computed. Since for such a LP one cannot expect to find an optimal solution in a finite number of operations, we present two schemes for finite approximations and establish the convergence of optimal values and policies for both the discounted and the expected average cost, with unbounded cost. Sometimes it turns to be easier to solve the problem with infinite state space than the problem with finite yet large state space. Based on the optimal policy for the problem with infinite state space, we construct policies which are almost optimal for the problem with truncated state space. This method is applied to obtain an e-optimal policy for a problem of optimal priority assignment under constraints for a system of K finite queues.