A proactive task dispatching method based on future bottleneck prediction for the smart factory
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.
Keywords: Smart factory; bottleneck prediction; dispatching method; production
Document Type: Research Article
Affiliations: 1: Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, Xi’an, China 2: Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong, China 3: Department of Mechanical Engineering, The University of Auckland, Auckland, New Zealand
Publication date: 04 March 2019
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