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Paper Type
Complete
Paper Number
1193
Description
Current methods of generating production schedules are either time-consuming or do not meet all productivity goals. A solution to this is to facilitate machine learning to generate production schedules. The necessary data to use machine learning is provided by the increasing use of cyber-physical systems in production environments. However, the mere acquisition of mass data through cyber-physical systems does not inherently lead to improvements. The algorithms must learn from historical situations to improve future calculations. Therefore, we implement a machine learning algorithm for real-time production scheduling in this paper. For this, we identify a suitable machine learning algorithm and meta-requirements based on a design science research approach. Finally, we evaluate the created artifact in a simulation study. The results show that the implemented solution can create schedules in real-time, which are of higher quality compared to priority rules.
Recommended Citation
Groth, Michael; Freier, Pascal; and Schumann, Matthias, "Using Self-Play within Deep Q Learning to improve real-time Production Scheduling" (2021). AMCIS 2021 Proceedings. 3.
https://aisel.aisnet.org/amcis2021/art_intel_sem_tech_intelligent_systems/art_intel_sem_tech_intelligent_systems/3
Using Self-Play within Deep Q Learning to improve real-time Production Scheduling
Current methods of generating production schedules are either time-consuming or do not meet all productivity goals. A solution to this is to facilitate machine learning to generate production schedules. The necessary data to use machine learning is provided by the increasing use of cyber-physical systems in production environments. However, the mere acquisition of mass data through cyber-physical systems does not inherently lead to improvements. The algorithms must learn from historical situations to improve future calculations. Therefore, we implement a machine learning algorithm for real-time production scheduling in this paper. For this, we identify a suitable machine learning algorithm and meta-requirements based on a design science research approach. Finally, we evaluate the created artifact in a simulation study. The results show that the implemented solution can create schedules in real-time, which are of higher quality compared to priority rules.
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