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By Gnaedig P., Honyek G., Riley K.F.

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Please use the foUowing format when citing this chapter: Leitao. , Restivo. , 2006, in IFIP International Federation for Information Processing, Voliune 220. Information Technology for Balanced Manufacturing Systems, ed. , (Boston; Springer), pp. 31-A6. 38 Information Technology for Balanced Manufacturing Systems In such dynamic environments, the optimised schedule produced by the front office can quickly become unacceptable, requiring the dynamic re-scheduling, as fast as possible, and done in a short amount of time, to avoid the risk of degradation of the production productivity.

London: Taylor & Francis, 2001. 12. Stadtler H, Kiiger C. Stipply Chain Management and Advanced Planning - Concepts, Models, Software and Case Studies. Berlin: Springer, 2000. 13. Stadtler H. Supply chain management and advanced planning-basics, overview and challenges. Eur J Oper Res 2005; 163(3): 575-588. 14. Tharumarajah A. Survey of resource allocation methods for distributed manufacturing systems. Prod Plan Control 2001; 12(1): 58-68. 15. Weiss G. Multiagent Systems A modern Approach to Distributed Artificial Intelligence.

The dynamic scheduling model is the result of the dynamic interaction between task, operational and supervisor holons, combining the problem solving at the individual holon level and the coordination-negotiation schema at the system level, to produce a global manufacturing scheduling, which is simultaneously centralized in normal situations or locally produced in the presence of a disturbance. The self-organization capability is the key concept to support this adaptive production control and scheduling mechanism.

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