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Integration of Discrete Simulation, Prediction, and Optimization Methods for a Production Line Digital Twin Design.

Damian Krenczyk1, Iwona Paprocka1

  • 1Department of Engineering Processes Automation and Integrated Manufacturing Systems, Silesian University of Technology, Konarskiego 18A Str., 44-100 Gliwice, Poland.

Materials (Basel, Switzerland)
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Summary
This summary is machine-generated.

This study introduces a smart factory concept using discrete simulations and AI for flexible production. It demonstrates a Digital Twin achieving optimal scheduling and reliable predictions for manufacturing, enhancing Industry 4.0 practices.

Keywords:
Digital TwinIndustry 4.0ant colony optimizationdiscrete event simulationintegrationmachine reliabilityprediction

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Area of Science:

  • Industrial Engineering
  • Artificial Intelligence
  • Operations Research

Background:

  • Achieving high production system flexibility requires integrating discrete simulations, AI, and probability theory.
  • Smart factory concepts necessitate robust data exchange, simulation, optimization, and predictive analysis.

Purpose of the Study:

  • To propose a smart factory operation concept integrating discrete simulation, AI, and probability.
  • To develop and evaluate an Ant Colony Optimization (ACO) algorithm for multi-criteria scheduling in a hybrid flow shop.
  • To demonstrate the application of a Digital Twin for predictive analysis and performance optimization in automotive manufacturing.

Main Methods:

  • Development of an Ant Colony Optimization (ACO) algorithm for scheduling.
  • Creation of a Digital Twin for a hybrid flow shop in the automotive industry.
  • Comparison of ACO with immune and genetic algorithms for production planning.
  • Prediction of reliability parameters (MTTF, MTTR) for production resources.

Main Results:

  • The Ant Colony Optimization (ACO) algorithm achieved optimal production schedules with minimal computation time using a Digital Twin.
  • Predicting reliability parameters for Digital Twin resources ensured stable production deadlines.
  • The developed Digital Twin provided measurable benefits in integrating simulation, data analysis, and optimization.

Conclusions:

  • The proposed integration of discrete simulation, AI, and Digital Twins bridges the gap between theory and practice in Industry 4.0.
  • The Ant Colony Optimization (ACO) algorithm is effective for multi-criteria scheduling, maximizing resource utilization and minimizing delays.
  • Predictive analysis of reliability parameters enhances the stability and predictability of manufacturing operations.