Research Group on Intelligent Manufacturing and Business Processes (IMBP)

Research and elaboration of techniques applicable for handling complex production and business systems working in an uncertain, changing environment, with special emphasis on artificial intelligence and machine learning approaches.

The mission formulated above is the overall aim of the Research Laboratory of Engineering and Management Intelligence consisting of closely co-operating research groups.

• Intelligent manufacturing processes and systems
• Modelling, control and optimisation of technical and business processes and process chains
• Management of changes and disturbances in complex systems
• Agent-based modelling and control of manufacturing systems, extended enterprises and production networks
• Machine learning approaches to technical and business problems
• Simulation of large-scale production and business systems; The Digital Enterprise

• The EU granted the prestigious title of Centre-of-Excellence to SZTAKI. The IMBP Research Group is involved in several Work Packages.

• Young Researcher Prize of Hungarian Academy of Sciences. Zsolt János Viharos for his work on "Intelligent techniques in the modelling and optimisation of manufacturing processes" received the prize from the Secretary-General of the Academy.

• Young Researcher Prize of SZTAKI. Péter Stefán received the prize in April, 2001.

• IEA/AIE-2001: The Fourteenth International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems, June 4-7, 2001, Budapest, Hungary (http://wwwold.sztaki.hu/conferences/ieaaie2001/).

Projects by the National Science Foundation, Hungary (OTKA)

• ESCM: An internet-based education/training platform in the field of supply chain management, for students, teachers and industrial employees

• Management of changes and disturbances in production structures, 1998 - 2001, L. Monostori
  

• Planning and control of production networks, OTKA, 2001 - 2004, L. Monostori

Projects supported by the National Committee for Technological Development (OMFB)

• R&D tools of the Digital Enterprise, OMFB, 2000-2001, L. Monostori

EU projects

• PLANET: European Network of Excellence in AI planning, ESPRIT LTR, 2000-, L. Monostori, J. Váncza

• AgentLink: European Network of Excellence for agent-based computing, ESPRIT LTR, (pending)

• VIMIMS: Virtual institute for the modelling of industrial manufacturing systems, SOCRATES-MINERVA, 2000-2002, L. Monostori, E. Ilie Zudor, B. Kádár

• Centre of Excellence of the EU, SZTAKI, 2000-, L. Monostori (2 work packages)

• E-Factory DNA, Products, Processes, Enterprises for competitiveness and sustainability of European Manufacturing Industry towards globalisation and new economy, EUREKA Cluster Project E!2319, L. Monostori

• VIPROMS: Virtual prototyping of manufacturing systems, 5th Framework, L. Monostori, B. Kádár (in preparation)

• IMS-NOE: European Network of Excellence in Intelligent Manufacturing Systems, L. Monostori, B. Kádár (in preparation)

National Research and Development Programs (NKFP)

• Digital factories, Production networks, L. Monostori (under evaluation)

Projects supported by the National Science Foundation, Hungary (OTKA)

• Fundamental research on intelligent manufacturing I, 1994 - 1996, L. Monostori

• Fundamental research on intelligent manufacturing II, 1995 - 1997, L. Monostori

• Intelligent techniques for reactive scheduling under rapidly changing, uncertain environment, 1995 - 1997, E. Szelke

• Subsymbolic and hybrid AI methods for financial and business applications, 1997 - 1999, L. Monostori

• Production structures with distributed intelligence, 1995 - 1997, B. Kádár

• Intelligent techniques in quality management, 1998 - 2000, Zs. Viharos

PHARE, COPERNICUS projects supported by the EU

• REMADE: Re-engineering of manufacturing processes through simulation and dynamic control techniques, PHARE, 1996 - 1997, L. Monostori

• IT tools for technology management, PHARE, 1996 - 1997, F. Erdélyi, L. Monostori

• DYCOMANS: Dynamic control and management systems in manufacturing processes, COPERNICUS, 1996 - 1997, L. Keviczky, L. Monostori

• DYCOMANS II: Dynamic control and management systems in manufacturing processes, COPERNICUS, 1998 - 2000, L. Keviczky, L. Monostori

EU projects, partially supported by the National Committee for Technological Development (OMFB)

• IMS: Intelligent manufacturing systems, ESPRIT LTR, 1997 - 2000, L. Monostori

• IiMB: Integration in manufacturing and beyond, ESPRIT LTR, 1996 - 2000, L. Monostori

• ICIMS-NOE: Network of Excellence in Intelligent control, and integrated manufacturing systems, ESPRIT LTR, 1996-, Gy. Kovács, L. Monostori

• Autonomous, co-operative systems in the production, OMFB, 1997 - 1999, L. Monostori

• Integration in Manufacturing, OMFB, 1997 - 2000, L. Monostori

[1] Monostori, L.; Barschdorff, D.: Artificial neural networks in intelligent manufacturing, Robotics and Computer-Integrated Manufacturing, Pergamon Press, Vol. 9, No. 6, 1992, pp. 421-437.

[2] Monostori, L.: A step towards intelligent manufacturing: Modeling and monitoring of manufacturing processes through artificial neural networks, Annals of the CIRP, Vol. 42, No. 1, 1993, pp. 485-488.

[3] Monostori, L.; Egresits, Cs.; Kádár, B.: Hybrid AI solutions and their application in manufacturing, Proceedings of the IEA/AIE-96, The Ninth International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems, June 4-7, 1996, Fukuoka, Japan, Gordon and Breach Publishers, pp. 469-478.

[4] Kádár, B.; Markos, S.; Monostori, L.: Knowledge based reactive management of manufacturing cells, Proceedings of the Conference on Integration in Manufacturing, Galway, Ireland, October 2-4, 1996, in: Advances in Design and Manufacturing: IT and Manufacturing Partnerships: Delivering the Promise, Edited by Browne, J.; Haendler Mas, R.; Hlodverson, O., pp. 197-205.

[5] Monostori, L.; Márkus, A.; Van Brussel, H.; Westkämper, E.: Machine learning approaches to manufacturing, Annals of the CIRP, Vol. 45, No. 2, 1996, pp. 675-712.

[6] Barschdorff, D.; Monostori, L.; Wöstenkühler, G.W.; Egresits, Cs.; Kádár, B.: Approaches to coupling connectionist and expert systems in intelligent manufacturing, Computers in Industry, Special Issue on Learning in Intelligent Manufacturing Systems, Vol. 33, No. 1, 1997, pp. 5-15.

[7] Monostori, L.; Egresits, Cs.: On hybrid learning and its application in intelligent manufacturing, Computers in Industry, Special Issue on Learning in Intelligent Manufacturing Systems, Vol. 33, No. 1, 1997, pp. 113-117.

[8] Mezgár, I.; Egresits, Cs.; Monostori, L.: Design and real-time reconfiguration of robust manufacturing systems by using design of experiments and artificial neural networks, Computers in Industry, Special Issue on Learning in Intelligent Manufacturing Systems, Vol. 33, No. 1, 1997, pp. 61-70.

[9] Monostori, L.; Hornyák, J.; Egresits, Cs.: ANN and hybrid AI approaches to financial and business problems, Neural Network World, International Journal on Neural and Mass-Parallel Computing and Information Systems, Vol. 7, No. 4, 5, 1997, pp. 495-506.

[10] Hornyák, J.; Monostori, L.: Feature extraction techniques for ANN-based financial forecasting, Neural Network World, International Journal on Neural and Mass-Parallel Computing and Information Systems, Vol. 7, No. 4, 5, 1997, pp. 543-552.

[11] Kádár, B.; Monostori, L.; Szelke, E.: An object oriented framework for developing distributed manufacturing architectures, Journal of Intelligent Manufacturing, Vol. 9, No. 2, April 1998, Special Issue on Agent Based Manufacturing, Chapman & Hall, pp. 173-179.

[12] Egresits, Cs.; Monostori, L.; Hornyák, J.: Multistrategy learning approaches to generate and tune fuzzy control structures and their applications in manufacturing, Journal of Intelligent Manufacturing, Vol. 9, No. 4, August 1998, Special Issue on Soft Computing Approaches to Manufacturing, Chapman & Hall, pp. 323-329.

[13] Monostori, L.; Hornyák, J.; Kádár, B.: Novel approaches to production planning and control, Proceedings of the First International Workshop on Intelligent Manufacturing Systems, IMS Europe 1988, April 15-17, 1998, Lausanne, Switzerland, pp. 115 - 132.

[14] Monostori, L.; Egresits, Cs.; Hornyák, J.; Viharos, Zs. J.: Soft computing and hybrid AI approaches to intelligent manufacturing, Lecture Notes in Artificial Intelligence, 1416, Tasks and Methods in Applied Artificial Intelligence, IEA/AIE-98, 11^th International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems, June 1-4, 1998, Benicassim, Castellon, Spain, Springer, Vol. II, pp. 765 - 774.

[15] Monostori, L.; Kádár, B.: Agent based architectures for mastering changes and disturbances in manufacturing, Lecture Notes in Artificial Intelligence, 1416, Tasks and Methods in Applied Artificial Intelligence, IEA/AIE-98, 11^th International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems, June 1-4, 1998, Benicassim, Castellon, Spain, Springer, Vol. II, pp. 755 - 764.

[16] Kádár B.; Monostori, L.: Agent based control of novel and traditional production systems, Proceedings of ICME98, CIRP International Seminar on Intelligent Computation in Manufacturing Engineering, July 1-3, 1998, Capri, Italy, pp. 31 - 38. (key-note paper)

[17] Hornyák, J.; Monostori, L.: Genetic algorithms for predictive and reactive scheduling of manufacturing systems, Proceedings of ICME98 CIRP International Seminar on Intelligent Computation in Manufacturing Engineering, July 1-3, 1998, Capri, Italy, pp. 213 - 220.

[18] Monostori, L.; Kádár, B.; Hornyák, J.: Approaches to manage changes and uncertainties in manufacturing, Annals of the CIRP, Vol. 47, No. 1, 1998, pp. 365 - 368.

[19] Monostori, L.; Szelke, E.; Kádár, B.: Management of changes and disturbances in manufacturing systems, Annual Reviews in Control, Pergamon Press, Elsevier Science, Vol. 22, 1998, pp. 85-97.

[20] Szelke, E.; Monostori, L.: Reactive scheduling in real-time production control, Chapter in: Modeling Manufacturing Systems, P. Bandimarte, A. Villa (editors), Springer, Berlin, Heidelberg, New York, 1999, pp. 65-113.

[21] Monostori, L.; Viharos, Zs. J.: Multipurpose modeling and optimization of production processes and process chains by combining machine learning and search techniques, Proceedings of The 32^nd CIRP International Seminar on Manufacturing Systems, New Supporting Tools for Designing Products and Production Systems, Leuven, Belgium, May 24-26, 1999, pp. 399-408.

[22] Viharos, Zs. J.; Monostori, L.: Automatic input-output configuration of ANN-based process models and its application in machining, Lecture Notes in Artificial Intelligence, Multiple Approaches to Intelligent Systems, IEA/AIE-99, 12^th International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems, Cairo, Egypt, May 31 - June 3, 1999, Springer, pp. 659-668.

[23] Monostori, L.; Kádár, B.: Holonic control of manufacturing systems, Preprints of the 1^st IFAC Workshop on Multi-Agent-Systems in Production, December 2-4, 1999, Vienna, Austria, pp. 109-114.

[24] Mezgár, I.; Monostori, L.; Kádár, B.; Egresits, Cs.: Knowledge-based hybrid techniques combined with simulation: Application to robust manufacturing systems, Chapter 25 in the book series: Knowledge-based Systems, Techniques and Applications, (Edited by C.R. Leondes), Academic Press, 2000, pp. 755-790.

[25] Monostori, L.; Kádár, B.; Viharos, Zs.J.; Mezgár, I.; Stefán, P.: Combined use of simulation and AI/machine learning techniques in designing manufacturing processes and systems, Proceedings of the 2000 International CIRP Design Seminar on Design with Manufacturing: Intelligent Design Concepts Methods and Algorithms, May 16-18, 2000, Haifa, Israel, pp. 199-204.

[26] Monostori, L.; Ilie-Zudor, E.: Environmental and life cycle issues in holonic manufacturing, Proceedings of The 33^rd CIRP International Seminar on Manufacturing Systems, June 5-7, 2000, Stockholm, Sweden, pp. 176-181.

[27] Bongaerts, L.; Monostori, L.; McFarlane, D.; Kádár, B.: Hierarchy in distributed shop floor control, Computers in Industry, Elsevier, Special Issue on Intelligent Manufacturing Systems, Vol. 43, No. 2, October 2000, pp. 123-137.

[28] Viharos, Zs. J.; Monostori, L.: Satisfying various requirements in different levels and stages of machining using one general ANN-based process model, Journal of Materials Processing Technology, Elsevier, Vol. 107, Nov. 22, 2000, pp. 228-235.

[29] Stefán, P.; Monostori, L.; Erdélyi, F.: Reinforcement learning for solving shortest-path and dynamic scheduling problems, Proceedings of the 3^rd International Workshop on Emergent Synthesis, IWES'01, March 12-13, 2001, Bled, Slovenia, pp. 83-88.

[30] Ilie-Zudor, E.; Monostori, L.: Agent-based support for handling environmental and life-cycle issues, IEA/AIE-01, 14^th International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems, Budapest, Hungary, June 4 - 7, 2001. (in print)

[31] Stefán, P.; Monostori, L.: On the relationship between learning capability and the Boltzmann-formula, IEA/AIE-01, 14^th International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems, Budapest, Hungary, June 4 - 7, 2001. (in print)

[32] Viharos, Zs. J.; Monostori, L.: Optimisation of process chains and production plants by using a hybrid, AI- and simulation based approach, IEA/AIE-01, 14^th International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems, Budapest, Hungary, June 4 - 7, 2001. (in print)

[33] Kádár, B.; Monostori, L.: Approaches to increase the performance of agent-based production systems, IEA/AIE-01, 14^th International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems, Budapest, Hungary, June 4 - 7, 2001. (in print)