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A CONWIP model for FMS control

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Abstract

Production inventory control is one of the most important aspects of a flexible manufacturing system (FMS) design. CONstant Work In Process (CONWIP), which is a hybrid of push-and-pull type systems, offers an alternative to effective utilization of the expensive FMS equipment while still meeting customer requirements. In the selection of an FMS control method, material handling often becomes one of the capacity constraints which forms the basis of various research interests. In this paper, a structure-based model for a CONWIP-controlled FMS is proposed, and within it, the node type characteristics concept is used to describe the constraints in FMS. Furthermore, simulation is used to determine the card number based on the structure-based model. The simulation results demonstrate that the model is suitable for the design and operation of FMS. The model can be used as a manufacturing execution system of enterprise resources planning. An architecture for this integrated design based on Internet/Intranet systems is also proposed.

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References

  • Bonvik, A. M., Dallery, Y. and Gershwin, S. B. (2000) Approximate analysis of production systems operated by a CONWIP/finite buffer hybrid control policy. International Journal of Production Research, 38, 2845-2869.

    Google Scholar 

  • Chan, F. T. S. and Smith, A. M. (1993) Simulation aids JIT-assembly line manufacturing—a case study. International Journal of Operations & Production Management, 13, 50-72.

    Google Scholar 

  • Chan, F. T. S. (1995) Using simulation to predict system performance: a case study of an electrophoretic deposition plant. Integrated Manufacturing Systems, 6, 27-38.

    Google Scholar 

  • Deleersnyder, J. L., Hodgson, T. J., Mueller(-Malek), H. and O'Grady, P. J. (1989) Kanban controlled pull system: an analysis approach. Management Science, 35, 1079-1091.

    Google Scholar 

  • Feng, S. C. (2000) Manufacturing planning and execution software interface. Journal of Manufacturing Systems, 19, 1-17.

    Google Scholar 

  • Gaury, E. G. A., Pierreval, H. and Kleijnen, J. P. C. (2000) An evolutionary approach to select a pull system among Kanban, CONWIP and Hybrid. Journal of Intelligent Manufacturing, 11, 157-167.

    Google Scholar 

  • Goldratt, E. M. and Fox, R. E. (1986) The Race, New York: North River Press, Croton-on-Hudson.

    Google Scholar 

  • Gstettner, S. and Kuhn, H. (1996) Analysis of production control systems Kanban and CONWIP. International Journal of Production Research, 34, 3253-3273.

    Google Scholar 

  • Gupta, A. (2000) Enterprise resource planning: the emerging organizational value system. Industrial Management and Data Systems, 100, 114-118.

    Google Scholar 

  • Hall, W. R. (1981) Driving the Productivity Machine: Production Planning and Control in Janpan, Falls Church, Virginia: American Production and Inventory Control Society.

    Google Scholar 

  • Herer, Y. T. and Masin, M. (1997) Mathematical programming formulation of CONWIP based production lines; and relationships to MRP. International Journal of Production Research, 35, 1067-1076.

    Google Scholar 

  • Holland, C. P. and Light, B. (1999) A critical success factors model for ERP implementation. IEEE Transactions on Software, May/June, 30-36.

  • Hopp, W. J. and Spearman, M. L. (1996) Factory Physics: Foundation of Manufacturing Management, Burr Ridge, IL: Irwin.

    Google Scholar 

  • Kazerooni, A., Chan, F. T. S. and Abhary, K. (1997) Real-time operation selection in an FMS using simulation—a fuzzy approach. Production Planning & Control, 8, 771-779.

    Google Scholar 

  • Košturiak, J. and Gregor, M. (1998) FMS simulation: some experience and recommendation. Simulation Practice and Theory, 6, 423-442.

    Google Scholar 

  • Kusiak, A. (2000) Computational Intelligence in Design and Manufacturing, New York: John Wiley.

    Google Scholar 

  • Leu, B. (2000) Generating a backlog list for a CONWIP production line: a simulation study. Production Planning & Control, 11, 409-418.

    Google Scholar 

  • Montazeri, M. and Van Wassenhove, L. N. (1990) Analysis of scheduling rules for an FMS. International Journal of Production Research, 28, 785-802.

    Google Scholar 

  • Ptak, C. A. (2000) ERP: Tools, Techniques, and Applications for Integrating the Supply Chain (Boca Raton, St. Lucie Press).

    Google Scholar 

  • Rao, S. S. (2000) Enterprise resource planning: business needs and technologies. Industrial Management and Data Systems, 100, 81-88.

    Google Scholar 

  • Spearman, M. L., Woodruff, D. L. and Hopp, W. J. (1990) CONWIP: a pull alternative to Kanban. International Journal of Production Research, 28, 879-894.

    Google Scholar 

  • Sugimori, Y., Kusunoki, K., Cho, F. and Uchikawa, S. (1977) Toyota production system and Kanban system materialization of just-in-time and respect-for-human system. International Journal of Production Research, 15, 553-564.

    Google Scholar 

  • Wang, D., Chen, X. and Li, Y. (1996) Experimental push/pull production planning and control system. Production Planning & Control, 7, 236-241.

    Google Scholar 

  • Wang, D. and Xu, C.-G. (1997) Hybrid push/pull production control strategy simulation and its applications. Production Planning & Control, 8, 142-151.

    Google Scholar 

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Ip, W.H., Yung, K.L., Huang, M. et al. A CONWIP model for FMS control. Journal of Intelligent Manufacturing 13, 109–117 (2002). https://doi.org/10.1023/A:1014532129642

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  • DOI: https://doi.org/10.1023/A:1014532129642

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