Real-Time Scheduling for Flexible Job Shop With AGVs Using Multiagent Reinforcement Learning and Efficient Action Decoding

Created by W.Langdon from gp-bibliography.bib Revision:1.8360

@Article{Li:2025:TSMC,

• author = "Yuxin Li and Qingzheng Wang and Xinyu Li and Liang Gao and Ling Fu and Yanbin Yu and Wei Zhou",
• title = "Real-Time Scheduling for Flexible Job Shop With {AGVs} Using Multiagent Reinforcement Learning and Efficient Action Decoding",
• journal = "IEEE Transactions on Systems, Man, and Cybernetics: Systems",
• year = "2025",
• volume = "55",
• number = "3",
• pages = "2120--2132",
• month = mar,
• keywords = "genetic algorithms, genetic programming, Job shop scheduling, Dynamic scheduling, Real-time systems, Production, Logistics, Conferences, Costs, Vehicle dynamics, Reinforcement learning, Metaheuristics, Automated guided vehicle (AGV), disturbance events, flexible job shop, multiagent reinforcement learning (MARL)",
• ISSN = "2168-2232",
• DOI = "doi:10.1109/TSMC.2024.3520381",
• abstract = "The application of automated guided vehicle (AGV) greatly improves the production efficiency of workshop. However, machine flexibility and limited logistics equipment increase the complexity of collaborative scheduling, and frequent dynamic events bring uncertainty. Therefore, this article proposes a real-time scheduling method for dynamic flexible job shop scheduling problem with AGVs using multiagent reinforcement learning (MARL). Specifically, a real-time scheduling framework is proposed in which a multiagent scheduling architecture is designed for achieving task selection, machine allocation and AGV allocation. Then, an action space and an efficient action decoding algorithm are proposed, which enable agents to explore in the high-quality solution space and improve the learning efficiency. In addition, a state space with generalisation, a reward function considering machine idle time and a strategy for handling four disturbance events are designed to minimise the total tardiness cost. Comparison experiments show that the proposed method outperforms the priority dispatching rules, genetic programming and four popular reinforcement learning (RL)-based methods, with performance improvements mostly exceeding 10percent. Furthermore, experiments considering four disturbance events demonstrate that the proposed method has strong robustness, and it can provide appropriate scheme for uncertain manufacturing system.",
• notes = "Also known as \cite{10829984}",

}

Genetic Programming entries for Yuxin Li Qingzheng Wang Xinyu Li Liang Gao Ling Fu Yanbin Yu Wei Zhou

Citations