A Deep Reinforcement Learning Approach for Production Scheduling with the Use of Dispatch Rules
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-57496-2_5
Reference20 articles.
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2. Ghaleb, M., Zolfagharinia, H., Taghipour, S.: Real-time production scheduling in the Industry-4.0 context: addressing uncertainties in job arrivals and machine breakdowns. Comput. Oper. Res. 123, 105031 (2020). https://doi.org/10.1016/J.COR.2020.105031
3. Lawler, E.L., Lenstra, J.K., Rinnooy Kan, A.H.G., Shmoys, D.B.: Chapter 9 Sequencing and scheduling: algorithms and complexity. Handbooks Oper. Res. Manage. Sci. 4(C), 445–522 (1993). https://doi.org/10.1016/S0927-0507(05)80189-6
4. Alexopoulos, K., Sipsas, K., Xanthakis, E., Makris, S., Mourtzis, D.: An industrial Internet of things based platform for context-aware information services in manufacturing. 31(11), 1111–1123 (2018). https://doi.org/10.1080/0951192X.2018.1500716
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