There are many mathematically-precise solutions available for FMS scheduling but most of them have not considered real-time changes or interruptions in the FMS such as machine breakdown, shortages of materials, rescheduling, etc. A more feasible solutions to the above problem is to construct stochastic models that can decribe the dynamics of the interruptions. The purpose of this paper is to ... [Show full abstract] investigate the methodology of using time-varying models, ARIMA (Autoregressive-Integrated-Moving-Average) models, to analyze the FMS interruptions. These models can be used to formulate the production rule-base of the FMS scheduler. Management can use this integrated approach to describe and predict the dynamic behavior of a complex FMS.