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Data-driven automated job shop scheduling optimization considering AGV obstacle avoidance.

Qi Tang1, Huan Wang2

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

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|January 2, 2025
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Summary
This summary is machine-generated.

This study optimizes automated job shops by integrating production and automated guided vehicle (AGV) scheduling. A novel two-layer model enhances collaboration and efficiency in production and transportation systems.

Keywords:
Automated job shopData-drivenObstacle avoidanceProduction scheduling

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Area of Science:

  • Operations Research
  • Industrial Engineering
  • Artificial Intelligence

Background:

  • Automated job shops rely heavily on automated guided vehicles (AGVs) for production efficiency.
  • Integrated planning of production and AGV operations is crucial for overall optimization.
  • Uncertainty in AGV transportation times poses a challenge to scheduling.

Purpose of the Study:

  • To propose a two-layer scheduling optimization model for simultaneous decision-making in batching, job sequencing, and AGV obstacle avoidance.
  • To enhance collaboration between production stages and AGV operation systems.
  • To address uncertainty in AGV transportation times and control delay risks.

Main Methods:

  • A data-driven Bayesian network method was used to model AGV transportation times based on historical data.
  • A time window mechanism was implemented to manage AGV delay risks.
  • An improved particle swarm optimization algorithm, incorporating genetic operators, was designed to solve the two-layer model.

Main Results:

  • The proposed model effectively improved collaboration between production stages and the AGV operation system.
  • The model successfully optimized a real-world scale case of an automated job shop.
  • Enhanced effectiveness of the integrated production and transportation system was demonstrated.

Conclusions:

  • The developed two-layer scheduling optimization model provides an effective solution for integrated production and AGV operations.
  • The data-driven approach using Bayesian networks and enhanced optimization algorithms improves system reliability and efficiency.
  • This research offers a practical framework for optimizing automated job shop performance.