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Unsupervised Learning for Opportunistic Maintenance Optimization in Flexible Manufacturing Systems

  • Published:2020 Issue: Volume:93 Page:1025-1030
  • ISSN:2212-8271
  • Container-title:Procedia CIRP
  • language:en
  • Short-container-title:Procedia CIRP

Author:

Wocker Michael,Betz Naomi Kimberly,Feuersänger Christian,Lindworsky Alexander,Deuse Jochen

Publisher

Elsevier BV

Subject

General Medicine

Reference37 articles.

1. Research on assembly module partition for flexible production in mass customization;Ren;Procedia CIRP,2018

2. Produktionsarbeit der Zukunft - Industrie 4.0: Studie;Ganschar,2013

3. Werkzeugmaschinen 1;Weck,2005

4. Flexible and reconfigurable manufacturing systems paradigms;ElMaraghy;Int J Flex Manuf Syst,2006

5. The global manufacturing revolution: Product-process-business integration and reconfigurable systems;Koren,2010

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1. Dynamic maintenance scheduling approach under uncertainty: Comparison between reinforcement learning, genetic algorithm simheuristic, dispatching rules;Expert Systems with Applications;2024-08

2. Opportunistic and delayed maintenance as strategies for sustainable maintenance practices;International Journal of Quality & Reliability Management;2024-06-28

3. Smart Master Production Scheduling by Deep Reinforcement Learning: An Exploratory Analysis;IFIP Advances in Information and Communication Technology;2024

4. Replay-Driven Continual Learning for the Industrial Internet of Things;2023 IEEE/ACM 2nd International Conference on AI Engineering – Software Engineering for AI (CAIN);2023-05

5. Flexible job shop scheduling with preventive maintenance consideration;Journal of Intelligent Manufacturing;2023-04-24
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