Reinforcement learning for opportunistic maintenance optimization
- 123 Downloads
Intelligent systems, that support the maintenance of production resources, offer real-time data-based approaches to optimize the maintenance effort and to reduce the usage of resources within production systems. However, unused potentials remain regarding maintenance schedules with minimal opportunity costs of the measures taken. This work provides a novel, machine-learning-based approach for the exploitation of these remaining optimization opportunities as an exemplary extension of the current state of the art. The determination of an optimal maintenance schedule for parallel working machines, is based on the data of a production system. The main result of this work is the performance of the implemented reinforcement learning algorithms, both in terms of downtime reduction, which increases the production output, and in terms of reducing maintenance costs compared to existing maintenance strategies. Hence, this work provides a holistic approach to the optimization of maintenance strategies and gives further evidence of a meaningful applicability of reinforcement learning algorithms in manufacturing processes.
KeywordsReinforcement learning Opportunistic maintenance Opportunity cost reduction Multi-agent-systems Proximal policy optimization Production planning and control
We extend our sincere thanks to the German Federal Ministry of Education and Research (BMBF) for supporting this research project 02K16C082 Produktionsbezogene Dienstleistungssysteme auf Basis von Big-Data-Analysen (ProData).
- 1.Wuest T (2016) Machine learning in manufacturing: advantages, challenges, and applications. Prod Manuf Res 4:23-45Google Scholar
- 7.Wang X et al (2014) Reinforcement learning based predictive maintenance for a machine with multiple deteriorating yield levels. J Comput Inf Syst 10(1):9–19Google Scholar
- 9.Sutton RS, Barto AG (2017) Reinforcement learning: an introduction. MIT Press, CambridgeGoogle Scholar
- 10.Crites RH, Barto AG (1995) Improving elevator performance using reinforcement-learning. Adv Neural Inf Process Syst 8:1017–1023Google Scholar
- 11.Mnih V, Kavukcuoglu K, Silver D, Graves A, Antonoglou I, Wierstra D, Riedmiller M (2013) Playing atari with deep reinforcement learning. CoRR. arXiv:abs/1312.5602
- 13.Schulman J, Levine S, Abbeel P, Jordan M, Moritz P (2015) Trust region policy optimization. In: Proceedings of the 31st International Conference on Machine Learning, vol 37, pp 1889–1897Google Scholar
- 14.Schulman J (2017) Proximal policy optimization algorithms. Adv Neural Inf Process Syst 8:1017–1023Google Scholar