Abstract
Industry 4.0 provides us with more precise real-time information about each factory element. Reinforcement Learning gives us new opportunities to improve old methodologies to resolve planning and scheduling problems using this information. Reinforcement Learning models can learn about old Master Plans and correct the mistakes that traditional algorithms cannot predict. This proposal improves the form to create a plan reducing the backlogged cost using Reinforcement Learning, specifically by means of a DQN Agent.
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Acknowledgments
Grant NIOTOME Ref. RTI2018–102020-B-I00 funded by MCIN/AEI/ https://doi.org/10.13039/501100011033 and ERDF A way of making Europe.
This research has been partially funded by the Agència Valenciana de la Innovació, under the program Proyectos Estratègics en Cooperació 2021 (exp. INNEST/2021/226.
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Terol, M., Gomez-Gasquet, P., Boza, A. (2023). Optimization Planning Scheduling Problem in Industry 4.0 Using Deep Reinforcement Learning. In: García Márquez, F.P., Segovia Ramírez, I., Bernalte Sánchez, P.J., Muñoz del Río, A. (eds) IoT and Data Science in Engineering Management. CIO 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 160. Springer, Cham. https://doi.org/10.1007/978-3-031-27915-7_26
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DOI: https://doi.org/10.1007/978-3-031-27915-7_26
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