Automatic Verification Flow Shop Scheduling of Electric Energy Meters Based on an Improved Q-Learning Algorithm

Long Peng, Jiajie Li, Jingming Zhao, Sanlei Dang, Zhengmin Kong and Li Ding
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Long Peng: Meteorology Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, China
Jiajie Li: Meteorology Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, China
Jingming Zhao: Meteorology Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, China
Sanlei Dang: Meteorology Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, China
Zhengmin Kong: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Li Ding: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China

Energies, 2022, vol. 15, issue 5, 1-11

Abstract: 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.

Keywords: reinforcement learning; Q-learning; flow shop scheduling; electric energy meters automatic verification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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