Agent-Based Generative Simulation of an Intelligent Distributed Scheduling World with Netlogo
Resumen
Unplanned disruptive events and disturbances such as arrivals of rush orders or machine breakdowns must be
managed locally to avoid propagating the effects along the value chain. To overcome the traditional separation
between task scheduling and manufacturing execution systems the novel idea of emergent synthesis/control of
schedules for better handling the dynamics at the shop-floor is proposed. A new interaction mechanism for
simultaneous distributed scheduling and execution control is evaluated using a generative simulation model in
Netlogo. The interaction mechanism has been designed around the concept of order and resource agents acting as
autonomic managers within the artificial society of a dynamic Gantt world. The advantages of generative modelling
in agent-based simulation are discussed to emphasize how difficult to predict emerging behaviours and bottom-up
macroscopic dynamics in a manufacturing case study can be addressed by proper design of agent interactions.
Results obtained for different abnormal scenarios are presented to highlight the benefits of simulating artificial
societies of intelligent agents.
Descargas
Citas
Valckenaers, P., Van Brussel, H. Holonic Manufacturing Execution Systems. CIRP Annals- Manufacturing
Technology, Vol. 54, (2005), pp. 427-432.
Verstraete, P. et al, Towards robust and efficient planning execution, Engineering Applications of Artificial
Intelligence, Vol. 21, (2008), pp. 304-314.
Leitao, P. et al., ADACOR: A collaborative production automation and control architecture, IEEE Intelligent
Systems, Vol. 20, (2005), pp. 58–66.
Rolón, M. et al. (2009), Agent Based Modelling and Simulation of Intelligent Distributed Scheduling Systems,
Proceedings of the 19th European Symposium on Computer Aided Process Engineering – ESCAPE19, Elsevier
B.V. (in press)
Kephart, J. O. and Chess, D. M. The vision of Autonomic Computing. Computer. Vol. 36, N° 1, (2003), pp. 41-
North, M. Macal, M. Managing Business Complexity, Discovering Strategic Solution with Agent-Based
Modeling and Simulation, Oxford: Oxford University Press. 2007.
Kletti, J. (Ed.). Manufacturing Execution Systems – MES. Berlin: Springer-Verlag. 2007.
Holonic Manufacturing System (HMS) consortium, http:// hms.ifw.uni-hannover.de/, Last access 05/02/2009.
Gilbert, N. Agent-based models. Gildford: Sage Publications. 2008.
Miller, J.H. and Page, S.E. Complex Adaptive Systems: An Introduction to Computational Models of Social
Life. Princeton: Princeton University Press. 2007.
Schelling, T. C. Micromotives and Macrobehavior. New York: Norton. 1978.
Epstein, J. M. Generative Social Science: Studies in Agent-Based Computational Modeling. Princeton:
Princeton University Press. 2006.
Wilensky, U. (1999), Netlogo Modeling Environment, available at http://ccl.northwestern.edu/netlogo, Last
access 05/02/2009.
Los artículos publicados en la Revista Ciencia y Tecnología son de exclusiva propiedad de sus autores. Las opiniones y el contenido de los mismos pertenecen a sus autores, declinando la Universidad de Palermo toda responsabilidad por los derechos que pudiera derivarse de la lectura y/o interpretación del contenido de los artículos publicados.
No se autoriza la reproducción, utilización ni explotación por parte de ningún tercero de los artículos publicados. Sólo se autoriza su uso para fines exclusivamente académicos y/o de investigación.
