Cyclic scheduling has been primarily studied under deterministic assumptions. In practice, stochastic variability exists and must be taken into account. In this paper, the descriptive Markov chain model of Bowman and Muckstadt is extended to cover demand variability. A production control algorithm is developed using cyclic time and task criticality estimates from the model. Application of the algorithm to a case study shows that material release and anticipatory inventory buildup decisions can be effective in reducing inventory holding and overtime costs when significant demand variability is present.
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