Agent-Based Generative Simulation of an Intelligent Distributed Scheduling World with Netlogo

  1. Rolon, Milagros
  2. Canavesio, Mercedes
  3. Martinez, Ernesto
Revista:
Ciencia y tecnología

ISSN: 1850-0870,  2344-9217

Año de publicación: 2009

Número: 9

Tipo: Artículo

DOI: 10.18682/CYT.V1I1.787 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Ciencia y tecnología

Objetivos de desarrollo sostenible

Resumen

Unplanned disruptive events and disturbances such as arrivals of rush orders or machine breakdowns must bemanaged locally to avoid propagating the effects along the value chain. To overcome the traditional separationbetween task scheduling and manufacturing execution systems the novel idea of emergent synthesis/control ofschedules for better handling the dynamics at the shop-floor is proposed. A new interaction mechanism forsimultaneous distributed scheduling and execution control is evaluated using a generative simulation model inNetlogo. The interaction mechanism has been designed around the concept of order and resource agents acting asautonomic managers within the artificial society of a dynamic Gantt world. The advantages of generative modellingin agent-based simulation are discussed to emphasize how difficult to predict emerging behaviours and bottom-upmacroscopic 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 artificialsocieties of intelligent agents.

Referencias bibliográficas

  • 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.
Fuente de los datos: Dialnet