Stochastic dynamic production control by neurodynamic programming

CIRP Annals - Manufacturing Technology

Volumn 55, Issue 1, 2006, Pages 473-478

  Monostori, L ^a,b   Csaji B Cs ^a  

^a INSTITUTE OF EXPERIMENTAL MEDICINE   (Hungary)

^b BUDAPEST UNIVERSITY OF TECHNOLOGY AND ECONOMICS   (Hungary)

Author keywords

Machine learning; Neurodynamic programming; Production control

Indexed keywords

DYNAMIC PROGRAMMING; ESTIMATION; FUNCTION EVALUATION; LEARNING SYSTEMS; MARKOV PROCESSES; OPTIMAL CONTROL SYSTEMS; STOCHASTIC PROGRAMMING;

MARKOV DECISION PROCESSES (MDP); NEURODYNAMIC PROGRAMMING; OPTIMAL CONTROL POLICY; Q-LEARNING;

PRODUCTION CONTROL;

EID: 33750085332     PISSN: 00078506     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0007-8506(07)60462-4     Document Type: Article

References (19)

1. Monostori, L., Markus, A., Van Brussel, H., West-kämper, E., 1996, Machine Learning Approaches to Manufacturing, Annals of the CIRP, 45/2:675-712.
2. Monostori, L., 2003, AI and Machine Learning Techniques for Managing Complexity, Changes and Uncertainties in Manufacturing, Engineering Applications of Artificial Intelligence, 16/4:277-291.
3. Ueda, K., Hatono, I., Fujii, N., Vaario, J, 2000, Reinforcement Learning Approaches to Biological Manufacturing Systems, Annals of the CIRP, 49/1:343-346.
4. Ueda, K., Markus, A., Monostori, L., Kais, H.J.J., Aral, T., 2001, Emergent Synthesis Methodologies for Manufacturing, Annals of the CIRP, 50/2:535-551.
5. Ueda, K., Lengyel, A., Hatono, I., 2004, Emergent Synthesis Approaches to Control and Planning in Make to Order Manufacturing Environments, Annals of the CIRP, 53/1:385-388.
6. Valckenaers, P., Van Brussel, H., 2005, Holonic Manufacturing Execution Systems, Annals of the CIRP, 54/1:427-432.
7. Csáji, B. Cs., Kader, B., Monostori, L., 2003, Improving Multi-Agent Based Scheduling by Neurodynamic Programming, HoloMAS-2003, Lecture Notes in Artificial Intelligence, Springer, 2744:110-123.
8. Monostori, L., Csáji, B. Cs., Kádár, B., 2004, Adaptation and Learning in Distributed Production Control, Annals of the CIRP, 53/1: 349-352.
9. Zhang, W., Dietterich, T., 1995, Reinforcement Learning Approach to Job-Shop Scheduling, Proceedings of the 14th International Joint Conference on Artificial Intelligence: 1114-1120.
10. Choi, J.Y.; Reveliotis, S., 2005, Relative Value Function Approximation for the Capacitated Re-entrant line scheduling Problem, IEEE Tr. on Automation Science and engineering, 2/3: 285-299.
11. Dietterich, T.G., Xin Wang, 2001, Batch Value Function Approximation via Support Vectors, Advances in Neural Information Processing Systems, 14: 1491-1498
12. Gersmann, K., Hammer, B., 2005, Improving Iterative Repair Strategies for Scheduling with the SVM, Neurocomputing, 63: 271-292.
13. Williamson, D.P., Hall, L.A., Hoogeveen, J.A., Hurkens, C.A.J., Lenstra, J.K., Sevastjanov, S.V., Shmoys, D.B., 1997, Short Shop Schedules, Operations Research, 45/2:288-294.
14. Bertsekas, D.P., Tsitsiklis J.N., 1996, Neuro-Dynamic Programming, Athena Scientific
15. Schölkopf, B., Smola, A., Williamson, R.C., Bartlett, P. L., 2000, New Support Vector Algorithms, Neural Computation, 12: 1207-1245.
16. th European Conference on Machine Learning: 282-294.
17. Chih-Chung, C., Chih-Jen, L., 2001, LIBSVM: A Library for Support Vector Machines, software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
18. Hurink, E., Jurisch, B., Thole, M., 1994, Tabu Search for the Job Shop Scheduling Problem with Multipurpose Machine, Operations Research Spektrum, 15: 205-215.
19. Monostori, L., Fornasiero, R., Váncza, J., 2005, Organizing and Running Real-time, Cooperative Enterprises, in: Advanced Manufacturing: An ICT and Systems Perspective (eds. Taisch, M., Thoben, K-D.), IMS: 144-157.