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Cranes are used extensively in manufacturing workshops to move jobs, but their high complexity and dynamics lead to difficult workshop production scheduling. To address this issue, this article proposes a deep reinforcement learning-based method combined with discrete event simulation to minimize the makespan of the double-deck traversable crane flexible job-shop scheduling problem (DTCFJSP). Specifically, the problem is first formulated as a finite Markov decision process by introducing state representation, an action space and a reward function. Then, a new double-deep Q-learning network is incorporated to create a selection strategy for optimal actions in different states. The results of experiments conducted in this study show that the average efficiency of the double-deck traversable crane is approximately 12% higher than that of regular cranes, and the application of deep reinforcement learning in crane scheduling is feasible and effective.

Intelligent scheduling of double-deck traversable cranes based on deep reinforcement learning