Solving Collaborative Scheduling of Production and Logistics via Deep Reinforcement Learning: Considering Limited Transportation Resources and Charging Constraints Article Swipe
Xianping Huang
,
Yong Chen
,
Wenchao Yi
,
Zhi Pei
,
Ziwen Cheng
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app15136995
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app15136995
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app15136995
- OA Status
- gold
- Cited By
- 1
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- 52
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4411471128Canonical identifier for this work in OpenAlex
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https://doi.org/10.3390/app15136995Digital Object Identifier
- Title
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Solving Collaborative Scheduling of Production and Logistics via Deep Reinforcement Learning: Considering Limited Transportation Resources and Charging ConstraintsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
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2025-06-20Full publication date if available
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Xianping Huang, Yong Chen, Wenchao Yi, Zhi Pei, Ziwen ChengList of authors in order
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https://doi.org/10.3390/app15136995Publisher landing page
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3390/app15136995Direct OA link when available
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Job shop scheduling, Computer science, Scheduling (production processes), Mathematical optimization, Reinforcement learning, Distributed computing, Artificial intelligence, Routing (electronic design automation), Computer network, MathematicsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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52Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| publication_date | 2025-06-20 |
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| referenced_works | https://openalex.org/W2157846217, https://openalex.org/W2046437335, https://openalex.org/W4407025393, https://openalex.org/W2937599159, https://openalex.org/W4205431000, https://openalex.org/W2260286117, https://openalex.org/W4388281316, https://openalex.org/W4401577501, https://openalex.org/W4404092074, https://openalex.org/W4405867375, https://openalex.org/W4408148356, https://openalex.org/W3004986338, https://openalex.org/W3092560364, https://openalex.org/W4363675709, https://openalex.org/W3169062181, https://openalex.org/W2291496492, https://openalex.org/W3209314316, https://openalex.org/W4408733447, https://openalex.org/W4407072906, https://openalex.org/W4402216097, https://openalex.org/W4404452010, https://openalex.org/W3128992509, https://openalex.org/W4282918345, https://openalex.org/W3181532009, https://openalex.org/W4312819473, https://openalex.org/W4387335068, https://openalex.org/W4381093805, https://openalex.org/W4407564366, https://openalex.org/W4408964777, https://openalex.org/W4408333325, https://openalex.org/W4408306285, https://openalex.org/W4410015697, https://openalex.org/W2529659779, https://openalex.org/W4385241110, https://openalex.org/W6808163342, https://openalex.org/W3135897879, https://openalex.org/W4313530516, https://openalex.org/W3172780424, https://openalex.org/W4391469576, https://openalex.org/W2966408967, https://openalex.org/W4409201768, https://openalex.org/W4223536612, https://openalex.org/W3194871405, https://openalex.org/W4284711157, https://openalex.org/W4390600666, https://openalex.org/W4226230002, https://openalex.org/W4285024875, https://openalex.org/W4309879851, https://openalex.org/W3209989022, https://openalex.org/W4409378839, https://openalex.org/W4409753347, https://openalex.org/W4210738826 |
| referenced_works_count | 52 |
| abstract_inverted_index.a | 67, 104 |
| abstract_inverted_index.KL | 111 |
| abstract_inverted_index.To | 60, 87 |
| abstract_inverted_index.an | 91, 143 |
| abstract_inverted_index.as | 32, 47 |
| abstract_inverted_index.in | 14, 25, 150 |
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| abstract_inverted_index.DRL | 136 |
| abstract_inverted_index.PPO | 93 |
| abstract_inverted_index.and | 22, 27, 35, 55, 78, 103, 118, 134, 148 |
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| abstract_inverted_index.model | 74 |
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| abstract_inverted_index.solve | 88 |
| abstract_inverted_index.their | 19 |
| abstract_inverted_index.which | 97 |
| abstract_inverted_index.(AGVs) | 9 |
| abstract_inverted_index.Guided | 7 |
| abstract_inverted_index.across | 138 |
| abstract_inverted_index.aiming | 81 |
| abstract_inverted_index.during | 120 |
| abstract_inverted_index.method | 129, 162 |
| abstract_inverted_index.policy | 121 |
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| abstract_inverted_index.target | 110 |
| abstract_inverted_index.widely | 12 |
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| abstract_inverted_index.enhance | 114 |
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| abstract_inverted_index.Finally, | 153 |
| abstract_inverted_index.However, | 38 |
| abstract_inverted_index.Vehicles | 8 |
| abstract_inverted_index.analysis | 155 |
| abstract_inverted_index.capacity | 58 |
| abstract_inverted_index.charging | 53, 56, 79 |
| abstract_inverted_index.clipping | 106 |
| abstract_inverted_index.consider | 43 |
| abstract_inverted_index.discrete | 28 |
| abstract_inverted_index.existing | 39 |
| abstract_inverted_index.flexible | 26, 68 |
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| abstract_inverted_index.minimize | 83 |
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| abstract_inverted_index.problem, | 90 |
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| abstract_inverted_index.proposes | 66 |
| abstract_inverted_index.updates. | 122 |
| abstract_inverted_index.verifies | 156 |
| abstract_inverted_index.Automated | 6 |
| abstract_inverted_index.achieving | 142 |
| abstract_inverted_index.assembly. | 37 |
| abstract_inverted_index.candidate | 99 |
| abstract_inverted_index.composite | 131 |
| abstract_inverted_index.logistics | 4 |
| abstract_inverted_index.makespan, | 151 |
| abstract_inverted_index.makespan. | 86 |
| abstract_inverted_index.mechanism | 107 |
| abstract_inverted_index.parameter | 166 |
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| abstract_inverted_index.capability | 117 |
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| abstract_inverted_index.mainstream | 135 |
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| abstract_inverted_index.robustness | 158 |
| abstract_inverted_index.scenarios, | 141 |
| abstract_inverted_index.scheduling | 73, 140 |
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| abstract_inverted_index.improvement | 145 |
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| abstract_inverted_index.sensitivity | 154 |
| abstract_inverted_index.technology, | 5 |
| abstract_inverted_index.Experimental | 123 |
| abstract_inverted_index.calculations | 102 |
| abstract_inverted_index.constraints, | 80 |
| abstract_inverted_index.incorporates | 76 |
| abstract_inverted_index.limitations. | 59 |
| abstract_inverted_index.particularly | 24 |
| abstract_inverted_index.collaborative | 72 |
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| abstract_inverted_index.manufacturing | 15, 29 |
| abstract_inverted_index.requirements, | 54 |
| abstract_inverted_index.respectively. | 152 |
| abstract_inverted_index.systematically | 42 |
| abstract_inverted_index.production-logistics | 71 |
| abstract_inverted_index.algorithm—CRGPPO-TKL—has | 94 |
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