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Computational-Intelligence-Based Scheduling with Edge Computing in Cyber-Physical Production Systems.

Changqing Xia1,2,3, Xi Jin1,2,3, Chi Xu1,2,3

  • 1State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.

Entropy (Basel, Switzerland)
|December 23, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a dynamic resource prediction scheduling (DRPS) method for cyber-physical production systems (CPPS). The DRPS method enhances job schedulability and real-time control in dynamic environments.

Keywords:
CPPSedge computingmanufacturingreal-timescheduling

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Area of Science:

  • Cyber-Physical Production Systems (CPPS)
  • Edge Computing
  • Computational Intelligence (CI)

Background:

  • Real-time performance and reliability are crucial for CPPS, necessitating solutions for complex job-shop scheduling (JSP) and resource reservation.
  • Traditional JSP methods struggle with dynamic conditions due to uncertain transmission and computation times.
  • Edge computing enables localized decisions in smart factories using CI methods, addressing challenges of traditional approaches.

Purpose of the Study:

  • To propose a dynamic resource prediction scheduling (DRPS) method based on CI for real-time, localized behavior-level control in CPPS.
  • To address the challenge of scheduling unexpected computing jobs with ultra-low latency and ultra-high reliability requirements.
  • To improve the acceptance ratio of unexpected jobs in dynamic CPPS environments.

Main Methods:

  • Developed a dynamic resource prediction scheduling (DRPS) method utilizing computational intelligence (CI).
  • Integrated a backpropagation neural network for predicting job arrival times.
  • Implemented real-time migration strategies based on resource analysis and human-computer interaction.

Main Results:

  • The proposed DRPS method demonstrates improved schedulability for unexpected computing jobs.
  • Experimental results show a 25.9% improvement in the job acceptance ratio compared to the earliest deadline first (EDF) scheme.
  • The DRPS method achieves real-time localized behavior-level control.

Conclusions:

  • The DRPS method effectively handles dynamic conditions and unexpected jobs in CPPS.
  • Edge computing, combined with CI and predictive scheduling, is vital for achieving ultra-low latency and high reliability.
  • The proposed approach enhances the efficiency and robustness of smart factory operations.