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This study presents a deep reinforcement learning method based on an attention-enhanced spliced heterogeneous graph neural network to address the distributed hybrid flow shop scheduling problem with degradation effect (DHFSP-DE). A spliced heterogeneous graph is designed to represent the complex topology of DHFSP-DE, capturing the interactions among operations, jobs, machines, and factories. To enhance the representation of scheduling states, this work constructs a feature extractor using a graph neural network with a heterogeneous composite attention mechanism that captures complex relationships among different node and edge types. Proximal Policy Optimization (PPO) is used to iteratively refine the scheduling policy and improve action selection. Empirical evaluations on multiple benchmarks show that the proposed model outperforms six widely used dispatching rules in terms of convergence speed and generalization capability.

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Reformulating Distributed Hybrid Flow Shop Scheduling Under Degradation Effect Using Deep Reinforcement Learning and Spliced Heterogeneous Graph Attention Networks

  • 会议论文

  • 发表日期：

    2025年

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  • Ran Wang,
  • Junqing Li

摘要

This study presents a deep reinforcement learning method based on an attention-enhanced spliced heterogeneous graph neural network to address the distributed hybrid flow shop scheduling problem with degradation effect (DHFSP-DE). A spliced heterogeneous graph is designed to represent the complex topology of DHFSP-DE, capturing the interactions among operations, jobs, machines, and factories. To enhance the representation of scheduling states, this work constructs a feature extractor using a graph neural network with a heterogeneous composite attention mechanism that captures complex relationships among different node and edge types. Proximal Policy Optimization (PPO) is used to iteratively refine the scheduling policy and improve action selection. Empirical evaluations on multiple benchmarks show that the proposed model outperforms six widely used dispatching rules in terms of convergence speed and generalization capability.