We consider a sequence of discounted cost, constrained Markov control processes (CCPs) with countable state space, metric action set and possibly unbounded cost functions. We give conditions under which the sequence of optimal values of the CCPs converges to the optimal value of a limiting CCP,...

We consider a sequence of discounted cost, constrained Markov control processes (CCPs) with countable state space, metric action set and possibly unbounded cost functions. We give conditions under which the sequence of optimal values of the CCPs converges to the optimal value of a limiting CCP,...

In this paper we consider Markov Decision Processes with discounted cost and a random rate in Borel spaces. We establish the dynamic programming algorithm in finite and infinity horizon cases. We provide conditions for the existence of measurable selectors. And we show an example of...

We consider Markov Decision Processes under light traffic conditions. We develop an algorithm to obtain asymptotically optimal policies for both the total discounted and the average cost criterion. This gives a general framework for several light traffic results in the literature. We illustrate...

In this note the Hamiltonian cycle problem is mapped into an infinite horizon discounted cost constrained Markov decision problem. The occupation measure based linear polytope associated with this control problem defines a convex set which either strictly contains or is equal to another convex...

We analyse several extensions to the Economic Order Quantity (EOQ) model: when the inventory deteriorates over time; when the demand contains a stock dependent term; and when the present value, or discounted cost, is included. We derive exact analytical expressions for the order that minimises...

In this paper we consider Markov Decision Processes with discounted cost and a random rate in Borel spaces. We establish the dynamic programming algorithm in finite and infinity horizon cases. We provide conditions for the existence of measurable selectors. And we show an example of...

We consider Markov Decision Processes under light traffic conditions. We develop an algorithm to obtain asymptotically optimal policies for both the total discounted and the average cost criterion. This gives a general framework for several light traffic results in the literature. We illustrate...