Abstract
The application of pattern recognition (PR) techniques, artificial neural networks (ANNs), and nowadays hybrid artificial intelligence (Al) techniques in manufacturing can be regarded as consecutive elements of a process started two decades ago. The fundamental aim of the paper is to outline the importance of soft computing and hybrid AI techniques in manufacturing by introducing a genetic algorithm (GA) based dynamic job shop scheduler and the integrated use of neural, fuzzy and GA techniques for modeling, control and monitoring purposes.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
Davis, L., Job shop scheduling with genetic algorithms, in Proc. of the International Conference on Genetic Algorithms and their Applications, Pittsburgh, Morgan Kaufman, (1985) 136–140.
Egresits, Cs.; Monostori, L.; Hornyák, J., Multistrategy learning approaches to generate and tune fuzzy control structures and their applications in manufacturing, Proceedings of the Second World Congress on Intelligent Manufacturing Processes and Systems, June 10–13, Budapest, Hungary, Springer, (1997) 88–94, also in Journal of Intelligent Manufacturing, Special Issue on Soft Computing Approaches to Manufacturing, Chapman and Hall, 1998, (in print)
Fang, Hsiao-Lan; Ross, P.; Come, D., A promising Genetic Algorithm approach to jobshop scheduling, rescheduling, and open-shop scheduling problems, Proc. of the Fifth International Conference on Genetic Algorithms, San Mateo, Morgan Kaufmann, (1993) 375–382.
Hatvany, J., The efficient use of deficient information, CIRP Annals, Vol. 32/1, (1983) 423–425.
Hatvany, J., Nemes, L.,. Intelligent manufacturing systems-a tentative forecast, In: A link between science and applications of automatic control, Proc. of the VIIth IFAC World Congress, (A. Niemi, (Ed.)), June 12–16, Helsinki, Finland, Vol. 2, (1978) 895–899.
Lin, C. H.; Lee, C. S. G., Neural-network-based fuzzy logic control and decision system, IEEE Trans. on Comp., Vol. 40, Dec., (1991) 1320–1336.
Monostori L.; Egresits Cs.; Kádár B., Hybrid AI solutions and their application in manufacturing, Proc. of IEA/AIE-96, The Ninth Int. Conf. on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems, June 4–7, 1996, Fukuoka, Japan, Cordon and Breach Publishers, (1996) 469–478.
Monostori, L., A step towards intelligent manufacturing: Modeling and monitoring of manufacturing processes through artificial neural networks, CIRP Annals, 42, No. 1, (1993) 485–488.
Monostori, L.; Egresits, Cs., On hybrid learning and its application in intelligent manufacturing. Preprints of the Second Int. Workshop on Learning in IMSs, Budapest, Hungary, April 20–21, (1995) 655–670, and Computers in Industry, Special issue on Learning in IMSs, Vol. 33, 1997, pp. 111–117.
Monostori, L.; Hornyák, J.; Kádár, B., Novel approaches to production planning and control, IMS Europe 1998, April 15–17, Lausanne, Switzerland (in print)
Monostori, L.; Márkus, A.; Van Brussel, H.; Westkämper, E., Machine learning approaches to manufacturing, Annals of the CIRP, Vol. 45, No. 2, (1996) 675–712.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Monostori, L., Hornyák, J., Egresits, C., Viharos, Z.J. (1998). Soft computing and hybrid AI approaches to intelligent manufacturing. In: Pasqual del Pobil, A., Mira, J., Ali, M. (eds) Tasks and Methods in Applied Artificial Intelligence. IEA/AIE 1998. Lecture Notes in Computer Science, vol 1416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64574-8_463
Download citation
DOI: https://doi.org/10.1007/3-540-64574-8_463
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64574-0
Online ISBN: 978-3-540-69350-5
eBook Packages: Springer Book Archive