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JACIII Vol.11 No.6 pp. 715-723
doi: 10.20965/jaciii.2007.p0715
(2007)

Paper:

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Advanced Multiple Product Flexible Manufacturing System Modelling Using Coloured Petri Net

Tauseef Aized*, Koji Takahashi**, and Ichiro Hagiwara*

*Hagiwara Lab, Department of Mechanical Sciences and Engineering, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552, Japan

**Takahashi Lab, Department of Electrical and Electronic Engineering, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552, Japan

Received:
January 12, 2007
Accepted:
March 19, 2007
Published:
July 20, 2007
Creative Commons License
Keywords:
flexible manufacturing system, coloured Petri net, resource breakdown, routing/machine flexibility, automated inspection
Abstract
The objective of this study is to analyse a pull type multi-product, multi-line and multi-stage flexible manufacturing system whose resources are subject to planned and unplanned breakdown conditions. To ensure a continual supply of the finished products, under breakdown conditions, the parts/materials move through alternate routes exhibiting routing flexibility. The machine resources are flexible in this study and are capable to produce more than one item. Every machining and assembly station has been equipped with automated inspection units to ensure the quality of the products. The system is modelled through coloured Petri net method and the impact of input factors has been shown on the performance of the system.
Cite this article as:
T. Aized, K. Takahashi, and I. Hagiwara, “Advanced Multiple Product Flexible Manufacturing System Modelling Using Coloured Petri Net,” J. Adv. Comput. Intell. Intell. Inform., Vol.11 No.6, pp. 715-723, 2007.
Data files:
References
  1. [1] R. G. Askin and C. R. Standridge, “Modelling and Analysis of Manufacturing System,” John Wiley and Sons, 1993.
  2. [2] D. Gupta and J. A. Buzacott, “A Framework for understanding flexibility of Manufacturing systems,” Journal of Manufacturing Systems, 8(2), pp. 89-95, 1989.
  3. [3] J. P. Shewchuk and C. L. Moodie, “Definition and classification of manufacturing flexibility types and measures,” International Journal of Flexible Manufacturing Systems, 10, pp. 325-349, 1998.
  4. [4] R. P. Parker and A. Wirth, “Theory and methodology – manufacturing flexibility: measures and relationships,” European Journal of Operation Research, 118, pp. 429-449, 1999.
  5. [5] A. K. Sethi and S. P. Sethi, “Flexibility in manufacturing: A survey,” International Journal of Flexible Manufacturing System, 2, pp. 289-328, 1990.
  6. [6] A. A. Desrochers and R. Y. Al-jaar, “Applications of Petri nets in manufacturing Systems: Modelling, Control and Performance Analysis,” IEEE Press, 1995.
  7. [7] H. Liu, Z. Jiang, F. Y. Lee, and Y. K. R. Fung, “Multiple-objective real-time-scheduler for semiconductor wafer fab using coloured timed object-oriented Petri net (CTOPN),” IEEE Int. Conf. Sys. Man and Cyber., Vol.1, pp. 510-515, Oct. 5-8, 2003.
  8. [8] J. P. Kenne and A. Gharbi, “A simulation optimization based control policy for failure prone one machine, two-product manufacturing systems,” Computers and Industrial Engineering, 46, pp. 285-292, 2004.
  9. [9] B. I. Abdallah, T. ElMekkaway, and A. H. ElMaraghy, “On deadlock-free scheduling in FMS,” Proc. IEEE Int. Conf. Sys. Man and Cyber, Vol.1, pp. 366-371, Oct. 11-14, 1998.
  10. [10] H. C. Kuo and P. H. Huang, “Failure modelling and process monitoring for flexible manufacturing systems using coloured timed Petri Nets,” IEEE Trans. on Rob. and Auto., Vol.16, No.3, pp. 301-312, June 2000.
  11. [11] W. Y. Kim, A. Inaba, T. Suzuki, and S. Okuma, “Scheduling of a large-scale production system based on a continuous and timed Petri net model,” IEICE Trans. Inf. & Sys., Vol.E86-D, No.3, pp. 583-593, March 2003.
  12. [12] R. Kumar, K. M. Tiwari, and A. Allada, “Modelling and rescheduling of a re-entrant wafer fabrication line involving machine unreliability,” International Journal of Production Research, Vol.42, No.21, pp. 4431-4455, Nov. 2004.
  13. [13] M. Hanna, “Modelling product quality in a machining centre using fuzzy Petri nets with neural networks,” Proc. IEEE Int. Conf. on Rob. & Auto., pp. 1502-1507, May 1999.
  14. [14] Z. Kasirolvalad, M. R. Jehad Motlagh, and M. A. Shadmani, “An intelligent modular modelling approach for quality control of CNC machines product using adaptive fuzzy Petri nets,” 8th Int. Conf. on Cont., Autom., Rob. and Vision, pp. 1342-1347, China, 2004.
  15. [15] M. Hanna, A. Buck, and R. Smith, “ Fuzzy Petri nets to model and control output quality from an flexible manufacturing cell,” Proc. 4th Int. Conf. on Computer Integ. Manufacturing and Automation, pp. 87-92, 1994.
  16. [16] P. Lutz, N. Djemel, and A. Bourjault, “Petri net modelling for multiproducts assembly systems including testing,” IEEE Int. Conf. on Rob. and Auto., Vol.2, pp. 1700-1750, 1994.
  17. [17] Y. T. ElMakkaway and A. H. ElMaraghay, “Real time scheduling with deadlock avoidance in flexible manufacturing systems,” International Journal of Advanced Manufacturing Technology, 22, pp. 259-270, 2003.
  18. [18] H. Liu, Z. Jiang, and R. Y. K. Fung, “Modelling of a large scale complex re-entrant manufacturing system by extended object oriented Petri nets,” International Journal of Advanced Manufacturing Technology, 27, pp. 190-204, 2005.
  19. [19] R. Li, Y. Shyu, and S. Adiga, “A heuristic rescheduling algorithm for computer-based production scheduling systems,” International Journal of Production Research, 31(8), pp. 1815-1826, 1993.
  20. [20] K. Jensen, “Coloured Petri Nets: Basic Concepts, Analysis Methods and Practical Use,” Vol.1, Springer-Verlag, 1992.
  21. [21] T. Aized, K. Takahashi, and I. Hagiwara, “Coloured Petri net based performance analysis of flexible manufacturing system with planned and unplanned resource breakdowns,” IEICE Technical Report, CAS 2005-61, CST 2005-30, pp. 1-6, November 2005.
  22. [22] S. Robinson, “A statistical process control approach for estimating the warm-up period,” Proc. Winter Simulation Conference, pp. 439-445, 2002.

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