Reinforcement learning approach to goal-regulation in a self-evolutionary manufacturing system

Expert Systems with Applications

Volumn 39, Issue 10, 2012, Pages 8736-8743

  Shin, Moonsoo ^a   Ryu, Kwangyeol ^b   Jung, Mooyoung ^c  

^a Hanbat National University   (South Korea)

^b Pusan National University   (South Korea)

^c Ulsan National Institute of Science and Technology   (South Korea)

Author keywords

Agent; Fractal organization; Goal regulation; Production planning; Reinforcement learning; Self evolutionary manufacturing system

Indexed keywords

CONTINUOUS PROCESS; DYNAMIC ORGANIZATION; FUZZY INFERENCE SYSTEMS; GOAL-ORIENTED; GOAL-REGULATION; MARKET DYNAMICS; NETWORK-BASED; PRODUCTION PLANNING; REGULATION MECHANISMS; REINFORCEMENT LEARNING APPROACH; REINFORCEMENT SIGNAL; SIMULATION STUDIES; WORKING MECHANISMS;

AGENTS; NEURAL NETWORKS; PRODUCTION CONTROL; REINFORCEMENT LEARNING;

MANUFACTURE;

EID: 84859217892     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2012.01.207     Document Type: Article

References (32)

1. Anderson, C. W. (1986). Learning and problem solving with multilayer connectionist systems. Ph.D. Thesis, University of Massachusetts.
2. F. Arredondo, and E. Martinez Learning and adaptation of a policy for dynamic order acceptance in make-to-order manufacturing Computers & Industrial Engineering 58 2010 70 83
3. A.G. Barto, R.S. Sutton, and C.W. Anderson Neuronlike adaptive elements that can solve difficult learning control problems IEEE Transactions on Systems, Man, and Cybernetics 13 5 1983 834 846
4. R.E. Bellman Dynamic programming 1957 Princeton University Press Princeton, NJ
5. H.R. Berenji An architecture for designing fuzzy controllers using neural networks International Journal of Approximate Reasoning 6 2 1992 267 292
6. H.R. Berenji, and P. Khedkar Learning and tuning fuzzy logic controllers through reinforcements IEEE Transactions on Neural Networks 3 5 1992 724 740
7. B.C. Csáji, L. Monostori, and B. Kádár Reinforcement learning in a distributed market-based production control system Advanced Engineering Informatics 20 2006 279 288
8. J.M. Frayret, S. D'Amours, and B. Montreuil Coordination and control in distributed and agent-based manufacturing systems Production Planning & Control 15 1 2004 42 54
9. S.S. Heragu, R.J. Graves, B. Kim, and A.St. Onge Intelligent agent based framework for manufacturing systems control IEEE Transactions on Systems, Man, and Cybernetics 32 5 2002 560 573
10. L. Jouffe Fuzzy inference system learning by reinforcement methods IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews 28 3 1998 338 355
11. P. Leitão, and F.J. Restivo ADACOR: A holonic architecture for agile and adaptive manufacturing control Computers in Industry 57 2 2006 121 130
12. M. Liu, Z. Sun, J. Yan, and J. Kang An adaptive annealing genetic algorithm for the job-shop planning and scheduling problem Expert Systems with Applications 38 2011 9248 9255
13. B.B. Mandelbrot The fractal geometry of nature 1982 Freeman New York
14. F. Maturana, W. Shen, and D.H. Norrie MetaMorph: An adaptive agent-based architecture for intelligent manufacturing International Journal of Production Research 37 10 1999 2159 2174
15. E. Mizutani Learning from reinforcement C. Jang, C. Sun, E. Mizutani, Neuro-Fuzzy and Soft Computing: a Computational Approaches to Learning and Machine Intelligence 1997 Prentice-Hall Upper Saddle River, NJ 258 300
16. H. Rau, and K. Cho Genetic algorithm modeling for the inspection allocation in reentrant production systems Expert Systems with Applications 36 2009 11287 11295
17. P. Renna, and M. Ambrico Evaluation of cellular manufacturing configurations in dynamic conditions using simulation International Journal of Advanced Manufacturing Technology 2011 10.1007/s00170-011-3255-0
18. D. Rumelhart, G. Hinton, and R.J. Williams Learning representations of back-propagation errors Nature 323 1986 533 536
19. K. Ryu, and M. Jung Goal-orientation mechanism in the fractal manufacturing system International Journal of Production Research 42 11 2004 2207 2225
20. K. Ryu, E. Yücesan, and M. Jung Dynamic restructuring process for self-reconfiguration in the fractal manufacturing system International Journal of Production Research 44 15 2006 3105 3129
21. W. Shen, F. Maturana, and D.H. Norrie MetaMorph II: An agent-based architecture for distributed intelligent design and manufacturing Journal of Intelligent Manufacturing 11 3 2000 237 251
22. M. Shin, Y. Cha, K. Ryu, and M. Jung Conflict detection and resolution for goal-formation in the fractal manufacturing system International Journal of Production Research 44 3 2006 447 465
23. M. Shin, J. Mun, and M. Jung Self-evolution framework of manufacturing systems based on fractal organization Computers & Industrial Engineering 56 2009 1029 1039
24. M. Shin, J. Mun, K. Lee, and M. Jung R-FrMS: A relation-driven fractal organisation for distributed manufacturing systems International Journal of Production Research 47 7 2009 1791 1814
25. Sutton, R.S. (1984). Temporal credit assignment in reinforcement learning. Ph.D. Thesis, University of Massachusetts.
26. R.S. Sutton, and A.G. Barto Reinforcement learning: An introduction 1998 MIT Press Cambridge
27. A.H. Tan, Y.S. Ong, and A. Tapanuj A hybrid agent architecture integrating desire, intention and reinforcement learning Expert Systems with Applications 38 7 2011 8477 8487
28. H. Van Brussel, J. Wyns, P. Valckenaers, L. Bongaerts, and P. Peeters Reference architecture for holonic manufacturing systems: PROSA Computers in Industry 37 1998 255 274
29. Y.C. Wang, and J.M. Usher Learning policies for single machine job dispatching Robotics and Computer-Integrated Manufacturing 20 2004 553 562
30. Y.C. Wang, and J.M. Usher A reinforcement approach for developing routing policies in multi-agent production scheduling International Journal of Advanced Manufacturing Technology 33 2007 323 333
31. G. Weiss Multiagent systems: A modern approach to distributed artificial intelligence 1999 MIT Press Cambridge
32. Z. Zhang, L. Zheng, and M.X. Weng Dynamic parallel machine scheduling with mean weighted tardiness objective by Q-learning International Journal of Advanced Manufacturing Technology 34 2007 968 980