Adaptive scheduling in random flexible manufacturing systems subject to machine breakdowns

In a FMS dynamic scheduling environment, frequent rescheduling to react to disruptions such as machine breakdowns can make the behaviour of the system hard to predict, and hence reduce the effectiveness of dynamic scheduling. Another approach to handle the disruptions is to update the job ready time and completion time, and machine status on a rolling horizon basis, and consider the machine availability explicitly in generating schedules. When machine downtime has a small variation, the operation completion time is estimated by using limiting (steady-state) machine availability. However, steady-state analysis is sometimes unlikely to provide a complete picture of the system when there is a large variation in machine downtime and repair time, and frequent disruptions (e.g. tool failures) exist. Transient analysis of machine availability will be more meaningful in such a situation during a finite observation period. In this paper, an adaptive scheduling approach is proposed to make coupled decisions about part/machine scheduling and operation/tool assignments on a rolling horizon basis, while the operation completion time is estimated by a transient machine availability analysis based on a two-state continuous time Markov process. The expected tool waiting time is explicitly considered in the job machine scheduling decision. The effectiveness of the proposed method is compared with other approaches based on various dispatching heuristics such as Apparent Tardiness Cost, Cost OVER Time, and Bottleneck Dynamics, etc under different shop load and machine downtime levels.