DQN-based aircraft overhaul workshop real-time scheduling method

A real-time scheduling and aircraft technology, applied in the direction of neural learning methods, instruments, biological neural network models, etc., can solve the problems of low stability of scheduling methods, poor self-adaptability, and unpredictable overhaul workshop environment, etc., and achieve high stability and adaptability, improve training speed, and shorten completion time

Active Publication Date: 2020-05-15
NORTHWESTERN POLYTECHNICAL UNIV
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AI Technical Summary

Problems solved by technology

However, as today's aircraft overhaul process becomes more and more complex, the environment of the overhaul workshop has become unpredictable, and the traditional scheduling method can no longer meet the needs of the overhaul process; the traditional scheduling method assigns tasks in advance without considering the real-time information of the workshop , leading to a large deviation between the plan and the actual production, such as the literature "Zhuang Xincun, Lu Yuhao, Li Congxin. Workshop Scheduling Problems Based on Genetic Algorithms [J]. Computer Engineering, 2006 (01): 199-200+203."
Especially in the complex aircraft overhaul workshop environment, the traditional scheduling method will face the problems of low stability and poor adaptability

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  • DQN-based aircraft overhaul workshop real-time scheduling method
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Embodiment Construction

[0022] The embodiments described below by referring to the figures are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0023] This implementation is a real-time scheduling method for aircraft overhaul based on DQN.

[0024] With the rapid development of IoT technology in aircraft overhaul workshops, a large amount of real-time data is available, which facilitates efficient real-time scheduling. However, many uncertainties in the workshop, such as machine failure, uncertain task processing time, etc., have brought serious challenges to real-time scheduling. To address these challenges, an efficient real-time scheduling method using DQN is proposed to minimize maketime for a flexible job shop employing IoT. The real-time scheduling problem is formulated as a Markov decision process. Then, a novel DQN-based real-time scheduling method is proposed to determine the optimal policy for this problem, throug...

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Abstract

The invention discloses a DQN-based aircraft overhaul real-time scheduling method, and belongs to the field of machine learning and intelligent manufacturing. Real-time information of an airplane overhaul workshop is obtained through the Internet of Things technology, and on the basis, a Markov decision model about the airplane overhaul workshop scheduling problem is established. The DQN method istrained by using the real-time information, so that the DQN method has a self-adaptive aircraft overhaul workshop real-time scheduling capability. And meanwhile, two Q networks with the same structure and an experience playback strategy are used for improving the training speed and the training effect of the DQN method. When the method is used for real-time scheduling of the aircraft overhaul workshop, the aircraft overhaul completion time can be effectively shortened.

Description

technical field [0001] The invention belongs to the field of machine learning and intelligent manufacturing, and in particular relates to a DQN-based real-time scheduling method for an aircraft overhaul workshop. Background technique [0002] In order to improve market competitiveness, aircraft overhaul enterprises must formulate reasonable scheduling strategies. Traditional aircraft overhaul workshop scheduling methods mainly focus on the application of traditional intelligent algorithms. However, as today's aircraft overhaul process becomes more and more complex, the environment of the overhaul workshop has become unpredictable, and the traditional scheduling method can no longer meet the needs of the overhaul process; the traditional scheduling method assigns tasks in advance without considering the real-time information of the workshop , leading to a large deviation between the plan and the actual production, such as the literature "Zhuang Xincun, Lu Yuhao, Li Congxin. W...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06Q10/00G06N3/08
CPCG06Q10/0631G06Q10/20G06N3/08Y02P90/30
Inventor 贾晓亮符式峰刘括胡昊孙冰洋
Owner NORTHWESTERN POLYTECHNICAL UNIV
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