Hierarchical Controls in Stochastic Manufacturing Systems with Machines in Tandem

This paper presents an asymptotic analysis of hierarchical production planning in a manufacturing system with serial machines that are subject to breakdown and repair, and with convex costs. The machines capacities are modeled as Markov chains. Since the number of parts in the internal buffers between any two machines needs to be non-negative, the problem is inherently a state constrained problem. As the rate of change in machines states approaches infinity, the analysis results in a limiting problem in which the stochastic machines capacity is replaced by the equilibrium mean capacity. A method of "lifting" and "modification" is introduced in order to construct near optimal controls for the original problem by using near optimal controls of the limiting problem. The value function of the original problem is shown to converge to the value function of the limiting problem, and the convergence rate is obtained based on some a priori estimates of the asymptotic behavior of the Markov chains. As a result, an error estimate can be obtained on the near optimality of the controls constructed for the original problem