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A multi objective collaborative reinforcement learning algorithm for flexible job shop scheduling.

Jian Li1, Shifa Li2, Pengbo He2

  • 1School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang, 471000, China. li_jian@haust.edu.cn.

Scientific Reports
|July 2, 2025
PubMed
Summary

This study introduces a novel multi-objective reinforcement learning algorithm for flexible job shop scheduling. The proposed method enhances scheduling efficiency by optimizing makespan and energy consumption, outperforming existing algorithms.

Keywords:
Collaborative agent reinforcement learningFlexible job shop scheduling problemMarkov decision process

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Area of Science:

  • Operations Research
  • Artificial Intelligence
  • Manufacturing Systems Engineering

Background:

  • Flexible job shop scheduling is complex, with competing objectives like minimizing makespan and energy consumption.
  • Existing algorithms often struggle to balance these multiple objectives effectively.
  • Intelligent agent-based approaches offer potential for dynamic and efficient scheduling.

Purpose of the Study:

  • To develop a multi-objective collaborative intelligent agent reinforcement learning algorithm for flexible job shop scheduling.
  • To optimize both the makespan and total energy consumption simultaneously.
  • To enhance the overall scheduling efficiency and practicality in real-world scenarios.

Main Methods:

  • A mathematical model for flexible job shop scheduling optimization was established, incorporating makespan and energy consumption as objectives.
  • A disjunctive graph was used to represent state features for intelligent agents.
  • Two intelligent agents with encoder-decoder components were designed for simultaneous job and machine decision-making.
  • A multi-objective Markov decision-process training model was constructed using temporal difference rewards.

Main Results:

  • The proposed algorithm demonstrated superior performance compared to existing methods on standard instances.
  • Evaluation metrics including hypervolume, set coverage, and inverted generational distance confirmed the algorithm's effectiveness.
  • A real-world case study validated the practical applicability and efficiency of the developed method.

Conclusions:

  • The multi-objective collaborative intelligent agent reinforcement learning algorithm significantly improves flexible job shop scheduling.
  • The method effectively balances makespan and energy consumption, offering a practical solution for manufacturing.
  • This approach represents a substantial advancement in intelligent scheduling for complex production environments.