Abstract
In this work we present a Reinforcement Learning approach for the Flexible Job Shop Scheduling problem. The proposed approach follows the ideas of the hierarchical approaches and combines learning and optimization in order to achieve better results. Several problem instances were used to test the algorithm and to compare the results with those reported by previous approaches.
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Martínez, Y., Nowé, A., Suárez, J., Bello, R. (2011). A Reinforcement Learning Approach for the Flexible Job Shop Scheduling Problem. In: Coello, C.A.C. (eds) Learning and Intelligent Optimization. LION 2011. Lecture Notes in Computer Science, vol 6683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25566-3_19
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DOI: https://doi.org/10.1007/978-3-642-25566-3_19
Publisher Name: Springer, Berlin, Heidelberg
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