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.

为了及时识别和减轻制造系统中随机干扰的不利影响，提出了一种识别和减轻随机干扰的方法。在该方法中，干扰分为几类，每类又细分为几类事件。硬件系统检测到的干扰数据被标准化为10个等级，以进行更精确的监控。建立了评估模型，该模型具有双层标准系统，可以同时评估每个类别的干扰和整个系统。模糊层次分析法用于计算标准权重并评估干扰的影响。用作约束的时间和成本被合并到调整解决方案中。BP神经网络用于为扰动生成调整解，然后调整资源和任务调度程序以减轻由干扰引起的损失。最后，通过实例对提出的方法进行了说明，验证了该方法的有效性。