We consider a problem of dynamic replenishment of parts in the supply chain consisting of single class of customers, company, and supplier. Customers request a service via the WEB-based ordering system and the company supports service using parts which are procured from the supplier. The...

We consider discrete time Markov Decision Process (MDP) with finite state and action spaces under average reward optimality criterion. The decomposition theory, in Ross and Varadarajan [11], leads to a natural partition of the state space into strongly communicating classes and a set of states...

In this paper we consider the weighted reward Markov decision process, with perturbation. The “weighted reward” refers to appropriately normalized convex combination of the discounted and the long-run average reward criteria. This criterion allows the controller to trade-off short-term costs...

In this paper, the optimization problem for a stopped Markov decision process with finite states and actions is considered over stopping times τ constrained so that ?τ≦α for some fixed α0. The problem is solved through randomization of stopping times and mathematical programming...

For a system of finite queues, we study how servers should be assigned dynamically to stations in order to obtain optimal (or near-optimal) long-run average throughput. We assume that travel times between different service facilities are negligible, that each server can work on only one job at a...

Every week, motion picture exhibitors must decide whether to keep or replace the movies playing in their theaters in light of the past week's sales data. This decision is complex because of the dynamic decision environment, the uncertainty of demand, the complex revenue-sharing terms between the...

In this paper, the optimization problem for a stopped Markov decision process with finite states and actions is considered over stopping times τ constrained so that ?τ≦α for some fixed α0. The problem is solved through randomization of stopping times and mathematical programming...