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<p>With the continuous development of intelligent manufacturing, automated guided vehicles (AGVs) have become a crucial component of transportation resources within workshops. Consequently, explicitly incorporating AGV transportation time into workshop scheduling is of considerable practical importance. This paper investigates the flexible job-shop scheduling problem with AGV transportation time (FJSP-AGV). First, a hierarchical action-space structure is proposed to address the multi-action decision-making challenge inherent in the FJSP-AGV. Second, an integer programming model is established for the FJSP-AGV, aiming to minimize the makespan. Subsequently, FJSP-AGV is modeled as a multi-agent Markov decision process (MMDP), in which a combination of a graph isomorphism network (GIN) and a multilayer perceptron is employed to encode and decode the state information of operations, machines, and AGVs. The Proximal Policy Optimization (PPO) algorithm is used to optimize the performance of the decision model. Finally, the trained model is evaluated on both benchmarks and randomly generated large-scale instances. For standard benchmark instances, the proposed approach achieves a 7% reduction in makespan compared with the best conventional dispatching rule. For randomly generated instances, the proposed approach reduces the makespan by approximately 13% relative to the optimal combined dispatching rules.</p>

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Research on the flexible job-shop scheduling problem considering AGV transport time based on proximal policy optimization with a graph isomorphism network

  • 期刊论文

  • 发表日期：

    2026年5月30日

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  • Minghai Yuan,
  • Yang Ye,
  • Liang Zheng,
  • Zhen Zhang,
  • Fengque Pei,
  • Yiyong Han

摘要

With the continuous development of intelligent manufacturing, automated guided vehicles (AGVs) have become a crucial component of transportation resources within workshops. Consequently, explicitly incorporating AGV transportation time into workshop scheduling is of considerable practical importance. This paper investigates the flexible job-shop scheduling problem with AGV transportation time (FJSP-AGV). First, a hierarchical action-space structure is proposed to address the multi-action decision-making challenge inherent in the FJSP-AGV. Second, an integer programming model is established for the FJSP-AGV, aiming to minimize the makespan. Subsequently, FJSP-AGV is modeled as a multi-agent Markov decision process (MMDP), in which a combination of a graph isomorphism network (GIN) and a multilayer perceptron is employed to encode and decode the state information of operations, machines, and AGVs. The Proximal Policy Optimization (PPO) algorithm is used to optimize the performance of the decision model. Finally, the trained model is evaluated on both benchmarks and randomly generated large-scale instances. For standard benchmark instances, the proposed approach achieves a 7% reduction in makespan compared with the best conventional dispatching rule. For randomly generated instances, the proposed approach reduces the makespan by approximately 13% relative to the optimal combined dispatching rules.