An Approximation for Kanban Controlled Assembly Systems

Abstract

An approximation is proposed to evaluate the steady-state performance of kanban controlled two-stage assembly systems. The development of the approximation is as follows. The considered continuous-time Markov chain is aggregated keeping the model exact, and this aggregate model is approximated replacing some state-dependent transition rates with constant rates. The approximate aggregate model is, then, decomposed into submodels and a product-form steady-state distribution is obtained for each submodel. Finally, the submodels are combined in such a way that the size of the problem becomes independent of the number of kanbans. This leads to the computational advantage in solving the combined model using numerical matrix-geometric solution algorithms. Numerical comparisons of the combined model with simulation, exact model, approximate aggregate model and an approximation in the literature show that the proposed approximation performs well in terms of accuracy and computational burden.