A proactive task dispatching method based on future bottleneck prediction for the smart factory

Abstract

The smart factory has been widely applied in manufacturing enterprises to meet dynamics in the global market. Bottleneck-based dispatching method (BDM) is a promising approach to improve the throughput of the system, which is mainly based on the current bottleneck. However, unexpected anomalies (e.g. order changes and machine failures) on shop-floor often lead to the bottleneck shifting which is hard to be tracked in traditional production shop-floor owing to the lack of real-time production data. To address the problem, a proactive task dispatching method based on future bottleneck prediction for a smart factory is proposed. Firstly, Internet of Things (IoT) technologies are applied to create a smart factory where manufacturing resources can be tracked and real-time and critical product data can be acquired to support accurate bottleneck prediction. Secondly, a bottleneck prediction method, that combines deep neural network (DNN) and time series analysis, is developed to predict future production bottleneck. Thirdly, based on the prediction, a future bottleneck-based dispatching method for throughput improvement is presented. Finally, several experiments are conducted to verify the effectiveness and availability of the proposed method.

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