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Multilayer Game Collaborative Optimization Based on Elman Neural Network System Diagnosis in Shared Manufacturing

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  • 1School of Management, Shenyang University of Technology, Shenyang 110870, China.

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This study introduces a robust optimization model for multienterprise dynamic equipment scheduling, enhancing profitability in shared manufacturing environments. The approach effectively manages demand and capacity uncertainties for improved industrial collaboration.

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Area of Science:

  • Manufacturing Systems Engineering
  • Operations Research
  • Artificial Intelligence

Background:

  • Modern manufacturing relies on collaborative production across multiple enterprises.
  • Dynamic sharing of global resources necessitates advanced scheduling and optimization methods.
  • Uncertainty in demand, capacity, and equipment performance challenges collaborative manufacturing.

Purpose of the Study:

  • To develop an optimized dynamic equipment collaborative scheduling system for multienterprise manufacturing.
  • To address uncertainties in demand, capacity, and equipment failure within a shared manufacturing model.
  • To maximize equipment utilization and profitability in a dynamic manufacturing network.

Main Methods:

  • Dynamic manufacturing network theory combined with neural network systems and model predictive control.
  • Multilayer game collaborative optimization robust model designed for uncertainty.
  • Inverse prediction of market demand using Elman neural networks and big data analysis.
  • Improved population intelligence algorithms for resource matching and flow optimization.

Main Results:

  • Model predictive control applied to Elman neural networks effectively mitigates adverse effects of uncertainty on profitability.
  • Robust optimization methods combined with dynamic equipment scheduling reduce worst-case cost loss by up to 24.73%.
  • The proposed model enhances idle equipment utilization and load capacity under shared manufacturing.

Conclusions:

  • The developed method offers significant practical value for intelligent production collaboration and industrial digital transformation.
  • Integrating predictive control and robust optimization is crucial for resilient and profitable collaborative manufacturing.
  • The approach provides a robust framework for managing complex dynamics in modern manufacturing networks.