Second six-monthly periodic report

Modelling industrial manufacturing systems: A method was developed to model complex production process and process chains. The method that combines simulation and artificial neural network techniques was applied in the optimization of process parameters [5]. We participate also in an international research consortium whose goal is to establish a virtual institute focused on knowledge sharing and integration of competencies in modelling and designing industrial manufacturing systems. This project applies on-line distance learning (ODL) techniques to develop a portal applicable both for research and educational purposes.
The follow-up of these works is a currently launched mid-term national R&D project on digital factories. The project, which is running with substantial industrial participation, focuses on resource-constrained project management, production planning, tele-presence and interactive multimedia, as well as on the monitoring of complex production processes.
SZTAKI helped to organize international conferences on the application of AI in manufacturing and engineering - such as the 2nd World Congress on Intelligent Manufacturing Processes and Systems [3] and IEA/AIE-2001, the 14th International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems [4].

References
[1] Bongaerts, L.; Monostori, L.; McFarlane, D.; Kádár, B.: Hierarchy in distributed shop floor control. Computers in Industry, 43(2),123-137, (2000).

[2] Márkus, A., Váncza, J.: Process planning with conflicting and conditional advice. Annals of the CIRP, 50(1), 327–330, (2001).

[3] Monostori, L. (ed.), Proc. of the 2nd World Congress on Intelligent Manufacturing Processes and Systems, Budapest, June 1997, Springer.

[4] Monostori, L., Váncza, J., Ali, M. (eds.): Proc. 14th Int. Conf. on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems, Budapest, June 2001, Springer LNAI 2070.

[5] Monostori, L.; Viharos, Zs.J.: Hybrid, AI- and simulation-supported optimisation of process chains and production plants. Annals of the CIRP, 50(1), 353-356, (2001).

[6] Ueda, K.; Márkus, A.; Monostori, L.; Kals, H.J.J.; Arai, T.: Emergent synthesis methodologies for manufacturing. Annals of the CIRP, 50(2), 535-551, (2001).

[7] Váncza, J., Márkus, A.: An agent model for incentive-based production scheduling. Computers in Industry, 43(2), 173–187, (2000).

[8] Váncza, J., Márkus, A.: A constraint engine for manufacturing process planning. In: Walsh, T. (ed.): Principles and Practice of Constraint Programming - CP 2001, Springer LNCS 2239, 745–759, (2001).

Links

www.sztaki.hu

www.sztaki.hu/ake/ai/

www.sztaki.hu/conferences/ieaaie2001/

http://www-lag.ensieg.inpg.fr/~vimims/main.htm

Laboratory of Engineering and Management Intelligence