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May 16, 2026Open Access

Adaptive distributed event-driven reinforcement learning for the dynamic flexible job shop scheduling problem

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Authors

RWRui Wu

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Implication

Randomized trial demonstrates improved scheduling performance in dynamic manufacturing, indicating effective adaptive policies.

Key Points

  • This work aims to improve scheduling efficiency in dynamic manufacturing environments using an adaptive RL framework.
  • Implemented a distributed event-driven reinforcement learning framework.
  • Evaluated three scenarios, including decentralized scheduling and machine breakdowns, using DDQN and PPO agents.
  • Applied multi-objective optimization techniques and analyzed with SHAP.
  • DDQN agent achieved the lowest mean tardiness and highest win rate with statistical significance over SPT.
  • PPO agent with feature-weighted prioritization outperformed standard scheduling methods in the presence of breakdowns.
  • UVFA-enhanced DDQN agent provided the best Pareto front for multi-objective settings, with consistent influence from waiting job features.

Cite This Study

Rui Wu (2026) studied this question.

synapsesocial.com/papers/6a0808afa487c87a6a40affdhttps://doi.org/10.5525/gla.thesis.85922
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Also Consider

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  1. 1Dynamic flexible job shop scheduling based on deep reinforcement learning2024 · 14 citations
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  3. 3Optimization Scheduling of Dynamic Industrial Systems Based on Reinforcement Learning2025 · 1 citations
  4. 4Adaptive manufacturing: dynamic resource allocation using multi-agent reinforcement learning2024 · 6 citations
  5. 5A deep reinforcement learning algorithm with heterogeneous graph and hierarchical attention mechanism for dynamic flexible job shop scheduling problem2026