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
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[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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