A multi objective collaborative reinforcement learning algorithm for flexible job shop scheduling
Abstract To improve the scheduling efficiency of flexible job shops, this paper proposes a multi-objective collaborative intelligent agent reinforcement learning algorithm based on weight distribution. First, a mathematical model for flexible job shop scheduling optimization is established, with the...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-03681-6 |
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| author | Jian Li Shifa Li Pengbo He Huankun Li |
| author_facet | Jian Li Shifa Li Pengbo He Huankun Li |
| author_sort | Jian Li |
| collection | DOAJ |
| description | Abstract To improve the scheduling efficiency of flexible job shops, this paper proposes a multi-objective collaborative intelligent agent reinforcement learning algorithm based on weight distribution. First, a mathematical model for flexible job shop scheduling optimization is established, with the makespan and total energy consumption of the shop as optimization objectives, and a disjunctive-graph is introduced to represent state features. Second, two intelligent agents are designed to address the simultaneous decision making problems of jobs and machines. Encoder-decoder components are implemented to facilitate state recognition and action output by the agents. Reward parameters are computed based on temporal differences at various moments, constructing a multi-objective Markov decision-process training model. Using hypervolume, set coverage and inverted generational distance as evaluation metrics, the algorithm is compared with those proposed in other studies on standard instances. The results demonstrate that the proposed method significantly outperforms other algorithms in solving the flexible job shop scheduling problem. Finally, a real-world case study further validates the effectiveness and practicality of the proposed algorithm. |
| format | Article |
| id | doaj-art-c0264ad048264767ade78bf23993e72f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c0264ad048264767ade78bf23993e72f2025-08-20T03:45:19ZengNature PortfolioScientific Reports2045-23222025-07-0115111810.1038/s41598-025-03681-6A multi objective collaborative reinforcement learning algorithm for flexible job shop schedulingJian Li0Shifa Li1Pengbo He2Huankun Li3School of Mechatronics Engineering, Henan University of Science and TechnologySchool of Mechatronics Engineering, Henan University of Science and TechnologySchool of Mechatronics Engineering, Henan University of Science and TechnologySchool of Mechatronics Engineering, Henan University of Science and TechnologyAbstract To improve the scheduling efficiency of flexible job shops, this paper proposes a multi-objective collaborative intelligent agent reinforcement learning algorithm based on weight distribution. First, a mathematical model for flexible job shop scheduling optimization is established, with the makespan and total energy consumption of the shop as optimization objectives, and a disjunctive-graph is introduced to represent state features. Second, two intelligent agents are designed to address the simultaneous decision making problems of jobs and machines. Encoder-decoder components are implemented to facilitate state recognition and action output by the agents. Reward parameters are computed based on temporal differences at various moments, constructing a multi-objective Markov decision-process training model. Using hypervolume, set coverage and inverted generational distance as evaluation metrics, the algorithm is compared with those proposed in other studies on standard instances. The results demonstrate that the proposed method significantly outperforms other algorithms in solving the flexible job shop scheduling problem. Finally, a real-world case study further validates the effectiveness and practicality of the proposed algorithm.https://doi.org/10.1038/s41598-025-03681-6Flexible job shop scheduling problemCollaborative agent reinforcement learningMarkov decision process |
| spellingShingle | Jian Li Shifa Li Pengbo He Huankun Li A multi objective collaborative reinforcement learning algorithm for flexible job shop scheduling Scientific Reports Flexible job shop scheduling problem Collaborative agent reinforcement learning Markov decision process |
| title | A multi objective collaborative reinforcement learning algorithm for flexible job shop scheduling |
| title_full | A multi objective collaborative reinforcement learning algorithm for flexible job shop scheduling |
| title_fullStr | A multi objective collaborative reinforcement learning algorithm for flexible job shop scheduling |
| title_full_unstemmed | A multi objective collaborative reinforcement learning algorithm for flexible job shop scheduling |
| title_short | A multi objective collaborative reinforcement learning algorithm for flexible job shop scheduling |
| title_sort | multi objective collaborative reinforcement learning algorithm for flexible job shop scheduling |
| topic | Flexible job shop scheduling problem Collaborative agent reinforcement learning Markov decision process |
| url | https://doi.org/10.1038/s41598-025-03681-6 |
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