Reinforcement learning for production ramp-up: A Q-batch learning approach

Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012

Volumn 1, Issue , 2012, Pages 610-615

  Doltsinis, Stefanos ^a   Ferreira, Pedro ^a   Lohse, Niels ^a  

^a UNIVERSITY OF NOTTINGHAM   (United Kingdom)

Author keywords

Decision Making Systems; Manufacturing; Ramp Up; Reinforcement Learning

Indexed keywords

AUTOMATED PRODUCTIONS; BATCH LEARNING APPROACH; DECISION-MAKING SYSTEMS; HUMAN DECISION MAKING; LEARNING APPROACH; PRODUCTION RAMP-UP; Q-LEARNING ALGORITHMS; RAMP-UP; REINFORCEMENT LEARNING APPROACH; SIGNIFICANT IMPACTS; SUPPORT TIME; TECHNICAL CHANGE; THEORETICAL FOUNDATIONS; TIME REDUCTION;

DECISION MAKING; LEARNING ALGORITHMS; MANUFACTURE;

REINFORCEMENT LEARNING;

EID: 84873607601     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLA.2012.113     Document Type: Conference Paper

References (17)

1. S. C. Doltsinis and N. Lohse, "A model-free reinforcement learningapproach using Monte Carlo method for production Ramp-up policyimprovement-A copy exactly test case," in 14th IFAC SYmposium onInformation Control Problems in Manufacturing, 2012, pp. 1628-1634.
2. C. Terwiesch and Y. Xu, "The Copy-Exactly Ramp-Up Strategy:Trading-Off Learning With Process Change," IEEE Transactions onEngineering Management, vol. 51, pp. 70-84, 2004.
3. J. E. Carrillo and C. Gaimon, "Improving manufacturing performancethrough process change and knowledge creation," ManagementScience, vol. 46, pp. 265-288, 2000.
4. S. Fjällström, K. Säfsten, U. Harlin, and J. Stahre, "Informationenabling production ramp-up," Journal of Manufacturing TechnologyManagement, vol. 20, pp. 178-196, 2009.
5. C. Terwiesch and R. E. Bohn, "Learning and process improvementduring production ramp-up," International Journal of ProductionEconomics., vol. 70pp, pp. 1-19, 2001.
6. L. Monostori, A. Markus, H. Van Brussel, and E. Westka mpfer,"Machine learning approaches to manufacturing," CIRP Annals-Manufacturing Technology, vol. 45, pp. 675-x7, 1996.
7. N. Lohse, H. Hirani, and S. Ratchev, "Equipment ontology formodular reconfigurable assembly systems," International Journal ofFlexible Manufacturing Systems, vol. 17, pp. 301-314, 2005.
8. M. Wiering and M. Van Otterlo, Reinforcement Learning: State-Of-The-Art: Springer, 2012.
9. X. Li, J. Wang, and R. Sawhney, "Reinforcement learning for jointpricing, lead-time and scheduling decisions in make-to-ordersystems," European Journal of Operational Research, vol. 221, pp.99-109, 2012.
10. Y. C. Wang and J. M. Usher, "Application of reinforcement learningfor agent-based production scheduling," Engineering Applications ofArtificial Intelligence, vol. 18, pp. 73-82, 2005.
11. M. Shin, K. Ryu, and M. Jung, "Reinforcement learning approach togoal-regulation in a self-evolutionary manufacturing system," ExpertSystems with Applications, vol. 39, pp. 8736-8743, 2012.
12. K. Ueda, I. Hatono, N. Fujii, and J. Vaario, "Reinforcement learningapproaches to Biological Manufacturing System," CIRP Annals-Manufacturing Technology, vol. 49, pp. 343-346, 2000.
13. S. Kalyanakrishnan and P. Stone, "Batch reinforcement learning in acomplex domain," 2007, pp. 662-669.
14. K. Konrad, M. Hoffmeister, M. Zapp, A. Verl, and J. Busse,"Enabling fast ramp-up of assembly lines through context-mapping ofimplicit operator knowledge and machine-derived data," vol. 371AICT, ed, 2012, pp. 163-174.
15. E. Project. (2012). Fast Ramp-up and Adaptive ManufacturingEnvironement (Frame). Available: http://www.frame-eu.org
16. M. L. Puterman, Markov Decision Processes: Discrete StochasticDynamic Programming, 2nd ed.: WILEY, 2005.
17. S. C. Doltsinis, Ratchev,S, Lohse, "A Framework for PerformanceMeasurement during Production Ramp-up of Assembly Stations,"Submitted to European Journal of Operational Research, 2012.