Automatic Verification Flow Shop Scheduling of Electric Energy Meters Based on an Improved Q-Learning Algorithm
Considering the engineering problem of electric energy meter automatic verification and scheduling, this paper proposes a novel scheduling scheme based on an improved Q-learning algorithm. First, by introducing the state variables and behavior variables, the ranking problem of combinatorial optimiza...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-02-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/15/5/1626 |
| _version_ | 1797475271874445312 |
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| author | Long Peng Jiajie Li Jingming Zhao Sanlei Dang Zhengmin Kong Li Ding |
| author_facet | Long Peng Jiajie Li Jingming Zhao Sanlei Dang Zhengmin Kong Li Ding |
| author_sort | Long Peng |
| collection | DOAJ |
| description | Considering the engineering problem of electric energy meter automatic verification and scheduling, this paper proposes a novel scheduling scheme based on an improved Q-learning algorithm. First, by introducing the state variables and behavior variables, the ranking problem of combinatorial optimization is transformed into a sequential decision problem. Then, a novel reward function is proposed to evaluate the pros and cons of the different strategies. In particular, this paper considers adopting the reinforcement learning algorithm to efficiently solve the problem. In addition, this paper also considers the ratio of exploration and utilization in the reinforcement learning process, and then provides reasonable exploration and utilization through an iterative updating scheme. Meanwhile, a decoupling strategy is introduced to address the restriction of over estimation. Finally, real time data from a provincial electric energy meter automatic verification center are used to verify the effectiveness of the proposed algorithm. |
| first_indexed | 2024-03-09T20:41:47Z |
| format | Article |
| id | doaj.art-58f0afd202784c4faedda28bcdfe8b24 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T20:41:47Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-58f0afd202784c4faedda28bcdfe8b242023-11-23T22:55:04ZengMDPI AGEnergies1996-10732022-02-01155162610.3390/en15051626Automatic Verification Flow Shop Scheduling of Electric Energy Meters Based on an Improved Q-Learning AlgorithmLong Peng0Jiajie Li1Jingming Zhao2Sanlei Dang3Zhengmin Kong4Li Ding5Meteorology Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, ChinaMeteorology Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, ChinaMeteorology Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, ChinaMeteorology Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, ChinaSchool of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, ChinaSchool of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, ChinaConsidering the engineering problem of electric energy meter automatic verification and scheduling, this paper proposes a novel scheduling scheme based on an improved Q-learning algorithm. First, by introducing the state variables and behavior variables, the ranking problem of combinatorial optimization is transformed into a sequential decision problem. Then, a novel reward function is proposed to evaluate the pros and cons of the different strategies. In particular, this paper considers adopting the reinforcement learning algorithm to efficiently solve the problem. In addition, this paper also considers the ratio of exploration and utilization in the reinforcement learning process, and then provides reasonable exploration and utilization through an iterative updating scheme. Meanwhile, a decoupling strategy is introduced to address the restriction of over estimation. Finally, real time data from a provincial electric energy meter automatic verification center are used to verify the effectiveness of the proposed algorithm.https://www.mdpi.com/1996-1073/15/5/1626reinforcement learningQ-learningflow shop schedulingelectric energy meters automatic verification |
| spellingShingle | Long Peng Jiajie Li Jingming Zhao Sanlei Dang Zhengmin Kong Li Ding Automatic Verification Flow Shop Scheduling of Electric Energy Meters Based on an Improved Q-Learning Algorithm Energies reinforcement learning Q-learning flow shop scheduling electric energy meters automatic verification |
| title | Automatic Verification Flow Shop Scheduling of Electric Energy Meters Based on an Improved Q-Learning Algorithm |
| title_full | Automatic Verification Flow Shop Scheduling of Electric Energy Meters Based on an Improved Q-Learning Algorithm |
| title_fullStr | Automatic Verification Flow Shop Scheduling of Electric Energy Meters Based on an Improved Q-Learning Algorithm |
| title_full_unstemmed | Automatic Verification Flow Shop Scheduling of Electric Energy Meters Based on an Improved Q-Learning Algorithm |
| title_short | Automatic Verification Flow Shop Scheduling of Electric Energy Meters Based on an Improved Q-Learning Algorithm |
| title_sort | automatic verification flow shop scheduling of electric energy meters based on an improved q learning algorithm |
| topic | reinforcement learning Q-learning flow shop scheduling electric energy meters automatic verification |
| url | https://www.mdpi.com/1996-1073/15/5/1626 |
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