Real-time scheduling for dynamic workshops with random new job insertions by using deep reinforcement learning

Dynamic real-time workshop scheduling on job arrival is critical for effective production. This study proposed a dynamic shop scheduling method integrating deep reinforcement learning and convolutional neural network (CNN). In this method, the spatial pyramid pooling layer was added to the CNN to ac...

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Bibliographic Details
Published in:Advances in production engineering & management 2023-06, Vol.18 (2), p.137-151
Main Authors: Sun, Z.Y., Han, W.M., Gao, L.L.
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Decision making
Deep learning
Dispatching rules
Energy consumption
Genetic algorithms
Heuristic
Job shops
Machine learning
Neural networks
Optimization
Production planning
Real time
Scheduling
Systems stability
Workshops
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Summary:Dynamic real-time workshop scheduling on job arrival is critical for effective production. This study proposed a dynamic shop scheduling method integrating deep reinforcement learning and convolutional neural network (CNN). In this method, the spatial pyramid pooling layer was added to the CNN to achieve effective dynamic scheduling. A five-channel, two-dimensional matrix that expressed the state characteristics of the production system was used to capture the state of the real-time production of the workshop. Adaptive scheduling was achieved by using a reward function that corresponds to the minimum total tardiness, and the common production dispatching rules were used as the action space. The experimental results revealed that the proposed algorithm achieved superior optimization capabilities with lower time cost than that of the genetic algorithm and could adaptively select appropriate dispatching rules based on the state features of the production system.
ISSN:1854-6250
1855-6531
DOI:10.14743/apem2023.2.462