Data-based real-time scheduling in smart manufacturing
【Abstract】The smart manufacturing system employs a large number of advanced information technologies, which makes it possible to collect real-time data in production systems. The wide application of various types of information technology in the manufacturing process has enabled the manufacturing system to accumulate a large amount of data relating to production scheduling. Therefore, the historical production scheduling data and the real-time production data collected by smart equipment are used to establish a data-driven production scheduling method. With regard to real-time hybrid flow shop scheduling problems, a real-time data-driven scheduling method based on the BP neural network is proposed. Firstly, the sample data for scheduling knowledge mining is extracted from the historical optimal and near-optimal scheduling scenarios. Through the BP neural network, the mapping relationship network between the production system state and the dispatching rules is obtained, which is then applied to online real-time scheduling. Finally, numerical experiments verify that the proposed method outperforms the fixed single dispatching rule, which is stable under different scheduling objectives.
【Keywords】 smart manufacturing; hybrid flow shop; real-time scheduling; machine learning; artificial neural network;
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