Adaptive control of stochastic manufacturing systems with hidden Markovian demands and small noise

The adaptive production planning of failure-prone manufacturing systems is considered. In real manufacturing systems, the product demand is usually not known a priori. One of the major tasks in production scheduling is to estimate and predict the demand. We consider the demand to be either the sum of an unknown rate and a small white noise or the sum of a hidden Markov chain and a small white noise. An algorithm is given to define a family of estimates for the unknown demand processes. Based on this family of estimates, adaptive controls are constructed, which are shown to be asymptotically optimal