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In order to timely identify and mitigate the adverse effect of the random disturbance in the manufacturing system, a method of identifying and mitigating the random disturbance is proposed. In the method, the disturbance is structured into several categories, and each is subdivided into several events. The disturbance data detected by the hardware system are normalized to 10 scales for more accurate monitoring. An evaluation model is built and it has a bi-layer criteria system, which can evaluate every category disturbance and the whole system at the same time. The fuzzy analytic hierarchy process is utilized to calculate the criteria weights and evaluate the impacts of the disturbance. Time and cost used as constraints are combined into the adjustment solution. The BP neural network is used to generate adjustment solution for the disturbance, and then the resource and task scheduler are adjusted to mitigate the loss caused by the disturbance. Finally, the proposed method is illustrated by an example, and the validity of the method is verified.

A real-time identification and mitigation solution generation method of random disturbance in a manufacturing system