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This paper evaluates four Reinforcement Learning (RL) algorithms, namely, Proximal Policy Optimization (PPO), Policy Gradient (PG), Advantage Actor-Critic (A2C), and Asynchronous Advantage Actor-Critic (A3C), for solving the Job Shop Scheduling Problem (JSSP) using Lawrence, Dermikol, and Taillard datasets. Experiments show that PPO consistently outperforms traditional dispatching rules and state-of-the-art methods, achieving 6–9 times lower optimality gaps than traditional algorithms and 2–3 times lower than state-of-the-art approaches across all datasets. These results demonstrate the potential of RL, particularly PPO, in enhancing scheduling optimization for the JSSP.

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Exploring Efficient Job Shop Scheduling Using Deep Reinforcement Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Reshma Maharjan,
  • Per-Arne Andersen,
  • Lei Jiao

摘要

This paper evaluates four Reinforcement Learning (RL) algorithms, namely, Proximal Policy Optimization (PPO), Policy Gradient (PG), Advantage Actor-Critic (A2C), and Asynchronous Advantage Actor-Critic (A3C), for solving the Job Shop Scheduling Problem (JSSP) using Lawrence, Dermikol, and Taillard datasets. Experiments show that PPO consistently outperforms traditional dispatching rules and state-of-the-art methods, achieving 6–9 times lower optimality gaps than traditional algorithms and 2–3 times lower than state-of-the-art approaches across all datasets. These results demonstrate the potential of RL, particularly PPO, in enhancing scheduling optimization for the JSSP.