Cost-Effective Inventory Control Under Risk

Abstract

Inventory systems play an important role in manufacturing and service providing organisations to support fluency of production or service. Evolution in time and environment of risk usually characterise operation of such systems. Proper control rules are required for decision making on replenishment orders. If stochastic dynamics of system motion exhibits the Markovian property, then Markov decision models can be employed to reveal a cost-effective inventory control policy. A single-item periodic-review inventory system in the maintenance centre of amanufacturing or transportation organisation with aPoisson arrival stream of demand requirements for spare components is introduced in the paper. The corresponding discrete-time Markov decision model with finite state and action spaces is described for sequential decisions on order sizes over an infinite planning horizon. The long-run expected average cost per unit time is the criterion of interest to be minimised by an optimal inventory control policy.