Flexible Job Shop Scheduling with Job Precedence Constraints: A Deep Reinforcement Learning Approach
The flexible job shop scheduling problem with job precedence constraints (FJSP-JPC) is highly relevant in industrial production scenarios involving assembly operations. Traditional methods, such as mathematical programming and meta-heuristics, often struggle with scalability and efficiency when solv...
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MDPI AG
2025-06-01
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| Series: | Journal of Manufacturing and Materials Processing |
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| author | Yishi Li Chunlong Yu |
| author_facet | Yishi Li Chunlong Yu |
| author_sort | Yishi Li |
| collection | DOAJ |
| description | The flexible job shop scheduling problem with job precedence constraints (FJSP-JPC) is highly relevant in industrial production scenarios involving assembly operations. Traditional methods, such as mathematical programming and meta-heuristics, often struggle with scalability and efficiency when solving large instances. We propose a deep reinforcement learning (DRL) approach to minimize makespan in FJSP-JPC. The proposed method employs a heterogeneous disjunctive graph to represent the system state and a multi-head graph attention network for feature extraction. An actor–critic framework, trained using proximal policy optimization (PPO), is adopted to make operation sequencing and machine assignment decisions. The effectiveness of the proposed method is validated through comparisons with several classic dispatching rules and a state-of-the-art DRL approach. Additionally, the contributions of key mechanisms, such as information diffusion, node features, and action space, are analyzed through a full factorial design of experiments. |
| format | Article |
| id | doaj-art-0b9edd10142b4c38bac4d3a63a6be99b |
| institution | Matheson Library |
| issn | 2504-4494 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Manufacturing and Materials Processing |
| spelling | doaj-art-0b9edd10142b4c38bac4d3a63a6be99b2025-07-25T13:26:40ZengMDPI AGJournal of Manufacturing and Materials Processing2504-44942025-06-019721610.3390/jmmp9070216Flexible Job Shop Scheduling with Job Precedence Constraints: A Deep Reinforcement Learning ApproachYishi Li0Chunlong Yu1School of Mechanical Engineering, Tongji University, Shanghai 201804, ChinaSchool of Mechanical Engineering, Tongji University, Shanghai 201804, ChinaThe flexible job shop scheduling problem with job precedence constraints (FJSP-JPC) is highly relevant in industrial production scenarios involving assembly operations. Traditional methods, such as mathematical programming and meta-heuristics, often struggle with scalability and efficiency when solving large instances. We propose a deep reinforcement learning (DRL) approach to minimize makespan in FJSP-JPC. The proposed method employs a heterogeneous disjunctive graph to represent the system state and a multi-head graph attention network for feature extraction. An actor–critic framework, trained using proximal policy optimization (PPO), is adopted to make operation sequencing and machine assignment decisions. The effectiveness of the proposed method is validated through comparisons with several classic dispatching rules and a state-of-the-art DRL approach. Additionally, the contributions of key mechanisms, such as information diffusion, node features, and action space, are analyzed through a full factorial design of experiments.https://www.mdpi.com/2504-4494/9/7/216flexible job shop schedulingjob precedence constraintsgraph neural networkdisjunctive graphdeep reinforcement learning |
| spellingShingle | Yishi Li Chunlong Yu Flexible Job Shop Scheduling with Job Precedence Constraints: A Deep Reinforcement Learning Approach Journal of Manufacturing and Materials Processing flexible job shop scheduling job precedence constraints graph neural network disjunctive graph deep reinforcement learning |
| title | Flexible Job Shop Scheduling with Job Precedence Constraints: A Deep Reinforcement Learning Approach |
| title_full | Flexible Job Shop Scheduling with Job Precedence Constraints: A Deep Reinforcement Learning Approach |
| title_fullStr | Flexible Job Shop Scheduling with Job Precedence Constraints: A Deep Reinforcement Learning Approach |
| title_full_unstemmed | Flexible Job Shop Scheduling with Job Precedence Constraints: A Deep Reinforcement Learning Approach |
| title_short | Flexible Job Shop Scheduling with Job Precedence Constraints: A Deep Reinforcement Learning Approach |
| title_sort | flexible job shop scheduling with job precedence constraints a deep reinforcement learning approach |
| topic | flexible job shop scheduling job precedence constraints graph neural network disjunctive graph deep reinforcement learning |
| url | https://www.mdpi.com/2504-4494/9/7/216 |
| work_keys_str_mv | AT yishili flexiblejobshopschedulingwithjobprecedenceconstraintsadeepreinforcementlearningapproach AT chunlongyu flexiblejobshopschedulingwithjobprecedenceconstraintsadeepreinforcementlearningapproach |