4.8 Article

A Proactive Manufacturing Resources Assignment Method Based on Production Performance Prediction for the Smart Factory

Journal

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 18, Issue 1, Pages 46-55

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2021.3073404

Keywords

Production; Manufacturing; Real-time systems; Analytical models; Predictive models; Industrial Internet of Things; Computational modeling; Key production performance prediction; manufacturing resources assignment (MRA); proactive decision making; self-adaptive optimization; smart factory

Funding

  1. Key Project of National Science Foundation of China [U2001201]
  2. National Science Foundation of China [51875266]
  3. Shaanxi Provincial Education Department [20JK0920]
  4. Key Research and Development Program of Shaanxi [2021GY-069]

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With the application of IIoT and CPS technologies, the manufacturing resources assignment has transformed from manual and passive mode to intelligent and active mode. A proactive manufacturing resources assignment method based on production performance prediction for the smart factory is proposed, which can accurately predict future production status and assign resources before production exceptions happen.
With the wide application of advanced industrial Internet of Things (IIoT) and cyber physical system (CPS) technologies, the manufacturing resources assignment method is transformed from manual and passive mode to intelligent and active mode. However, due to the lack of real-time analysis and accurate prediction of production performance, the production adjustment demands are often released after production exceptions happen, and production decisions are often made based on historical production information, which may lead to the problem of production interruption or performance reduction. To address this issue, a proactive manufacturing resources assignment (PMRA) method based on production performance prediction for the smart factory is proposed. First, the advanced IIoT and CPS technologies are applied to create a cloud-edge cooperation environment for a smart factory, where the resources are made smart with distributed control capacity, and cloud center and edge resources can collaborate dynamically. Second, a real-time colored Petri net enabled key production performance indicators analysis and prediction method are proposed to extract real-time production information and predict future production status accurately. Then, the PMRA method is presented to assign the resources before production exceptions happen. Finally, a case study from a typical manufacturer for computer numerical control machine tools in North China is used to validate the proposed method and results show that the proposed PMRA method can largely reduce the total tardiness and the total energy consumption.

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