Solving Collaborative Scheduling of Production and Logistics via Deep Reinforcement Learning: Considering Limited Transportation Resources and Charging Constraints
With the advancement of logistics technology, Automated Guided Vehicles (AGVs) have been widely adopted in manufacturing enterprises due to their high flexibility and stability, particularly in flexible and discrete manufacturing domains such as tire production and electronic assembly. However, exis...
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MDPI AG
2025-06-01
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| author | Xianping Huang Yong Chen Wenchao Yi Zhi Pei Ziwen Cheng |
| author_facet | Xianping Huang Yong Chen Wenchao Yi Zhi Pei Ziwen Cheng |
| author_sort | Xianping Huang |
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| description | With the advancement of logistics technology, Automated Guided Vehicles (AGVs) have been widely adopted in manufacturing enterprises due to their high flexibility and stability, particularly in flexible and discrete manufacturing domains such as tire production and electronic assembly. However, existing studies seldom systematically consider practical constraints such as limited AGV transport resources, AGV charging requirements, and charging station capacity limitations. To address this gap, this paper proposes a flexible job shop production-logistics collaborative scheduling model that incorporates transport and charging constraints, aiming to minimize the maximum makespan. To solve this problem, an improved PPO algorithm—CRGPPO-TKL—has been developed, which integrates candidate probability ratio calculations and a dynamic clipping mechanism based on target KL divergence to enhance the exploration capability and stability during policy updates. Experimental results demonstrate that the proposed method outperforms composite dispatching rules and mainstream DRL methods across multiple scheduling scenarios, achieving an average improvement of 8.2% and 10.5% in makespan, respectively. Finally, sensitivity analysis verifies the robustness of the proposed method with respect to parameter combinations. |
| format | Article |
| id | doaj-art-c361359608fb4b0fbaafd5874f36e23e |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-c361359608fb4b0fbaafd5874f36e23e2025-08-20T02:35:51ZengMDPI AGApplied Sciences2076-34172025-06-011513699510.3390/app15136995Solving Collaborative Scheduling of Production and Logistics via Deep Reinforcement Learning: Considering Limited Transportation Resources and Charging ConstraintsXianping Huang0Yong Chen1Wenchao Yi2Zhi Pei3Ziwen Cheng4School of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310012, ChinaSchool of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310012, ChinaSchool of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310012, ChinaSchool of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310012, ChinaSchool of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310012, ChinaWith the advancement of logistics technology, Automated Guided Vehicles (AGVs) have been widely adopted in manufacturing enterprises due to their high flexibility and stability, particularly in flexible and discrete manufacturing domains such as tire production and electronic assembly. However, existing studies seldom systematically consider practical constraints such as limited AGV transport resources, AGV charging requirements, and charging station capacity limitations. To address this gap, this paper proposes a flexible job shop production-logistics collaborative scheduling model that incorporates transport and charging constraints, aiming to minimize the maximum makespan. To solve this problem, an improved PPO algorithm—CRGPPO-TKL—has been developed, which integrates candidate probability ratio calculations and a dynamic clipping mechanism based on target KL divergence to enhance the exploration capability and stability during policy updates. Experimental results demonstrate that the proposed method outperforms composite dispatching rules and mainstream DRL methods across multiple scheduling scenarios, achieving an average improvement of 8.2% and 10.5% in makespan, respectively. Finally, sensitivity analysis verifies the robustness of the proposed method with respect to parameter combinations.https://www.mdpi.com/2076-3417/15/13/6995flexible job shop schedulingAGVslimited transportation resourcescharging constraintsDRLPPO |
| spellingShingle | Xianping Huang Yong Chen Wenchao Yi Zhi Pei Ziwen Cheng Solving Collaborative Scheduling of Production and Logistics via Deep Reinforcement Learning: Considering Limited Transportation Resources and Charging Constraints Applied Sciences flexible job shop scheduling AGVs limited transportation resources charging constraints DRL PPO |
| title | Solving Collaborative Scheduling of Production and Logistics via Deep Reinforcement Learning: Considering Limited Transportation Resources and Charging Constraints |
| title_full | Solving Collaborative Scheduling of Production and Logistics via Deep Reinforcement Learning: Considering Limited Transportation Resources and Charging Constraints |
| title_fullStr | Solving Collaborative Scheduling of Production and Logistics via Deep Reinforcement Learning: Considering Limited Transportation Resources and Charging Constraints |
| title_full_unstemmed | Solving Collaborative Scheduling of Production and Logistics via Deep Reinforcement Learning: Considering Limited Transportation Resources and Charging Constraints |
| title_short | Solving Collaborative Scheduling of Production and Logistics via Deep Reinforcement Learning: Considering Limited Transportation Resources and Charging Constraints |
| title_sort | solving collaborative scheduling of production and logistics via deep reinforcement learning considering limited transportation resources and charging constraints |
| topic | flexible job shop scheduling AGVs limited transportation resources charging constraints DRL PPO |
| url | https://www.mdpi.com/2076-3417/15/13/6995 |
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