Skip to main content

A Simultaneous Approach for IMS Design: a Possibility Based Approach

  • Chapter
Modeling Manufacturing Systems

Abstract

IMS investments are characterised by high fixed costs and long life cycles. On the other hand, their redditivity and risk coverage depend on their manufacturing efficiency that is mainly defined during the design phase by fixing the system configuration. Due to the flexibility required to IMS, system configuration depends not only from technological information, such as product routing table and service times, but also from marketing data such as the typology of products to be manufacture and their production volumes. Moreover, the evaluation of the redditivity and the risk of the investment depends on market information such as product prices as well. Such interdependencies make the investment decision environment very complex. Furthermore, the requirements and the data characterising the decision environment are affected by imprecision due to the strategic nature of the decision. Therefore, IMS investment decisions have to be made in a very complex and vague decision environment. Even if, several approaches have been proposed in literature to deal with IMS design problem, very few consider the specific characteristics of the decision environment. In this paper we propose a methodology and a tool that fit very well with the complexity and the vagueness of such design problem. The methodology consists into a simultaneous description of all the requirements, while the tool is the fuzzy possibility theory.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Antonsson, E. K., and Otto, K. N.., 1995., Imprecision in Engineering Design. ASME Journal of Mechanical Design Vol. 117.

    Google Scholar 

  2. Burstein M. C.. 1986. Finding the Economical Mix of Rigid and Flexible Automation for Manufacturing Systems. Proceedings of the 2nd ORSA/TIMS Conference on Integrated Manufacturing Systems: Operation Research Models and Applications. K. E. Stecke and R. Suri (eds.), Elsevier Science Publisher, Amsterdam.

    Google Scholar 

  3. Buzacott J. A. and Mandelbaum M.. 1983. Flexibility and Productivity in Manufacturing systems. Proceedings of the annual IIE Conference, Los Angeles CA.

    Google Scholar 

  4. Buzacott J. and Yao D.. 1986. Integrated Manufacturing Systems: A Review of Analytical Models. Management Science, Vol. 32, No. 7.

    Google Scholar 

  5. Carrie A.. 1988. Simulation of Manufacturing Systems John Wiley and Sons Ltd.

    Google Scholar 

  6. Dallery Y. and Frein Y.. 1986. An Efficient Method to Determine the Optimal Configuration of a Integrated Manufacturing System. Proceedings of the 2nd ORSA/TIMS Conference on Integrated Manufacturing Systems: Operation Research Models and Applications, K. E. Stecke and R. Suri (eds.), Elsevier Science Publisher, Amsterdam.

    Google Scholar 

  7. Davis, E.. 1987. Constraint propagation with interval labels. Artificial Intelligence, Vol. 32.

    Google Scholar 

  8. Dong, W. M. and Wong, F. S.. 1987. Fuzzy weighted averages and implementation of the extension principle. Fuzzy Sets and Systems, Vol. 24, No. 2.

    Google Scholar 

  9. Dubois D. and Prade H.. 1979. Fuzzy real algebra: Some results. Fuzzy Set and Systems, Vol. 2.

    Google Scholar 

  10. Dubois, D.. 1987. Ari Application of Fuzzy Arithmetic to the Optimization of Industrial Machining Processes. Mathematical Modelling, Vol. 9.

    Google Scholar 

  11. Dubois, D., and Prade H.. 1991. Fuzzy set in approximate reasoning, Part 1: Inference with possibility distribution. Fuzzy Sets and Systems, Vol. 40.

    Google Scholar 

  12. Finch, W.W. and Ward, A.C.. 1995. Generalized set-propagation operations over relations of more than three parameters. Artificial Intelligence in Engineering Design, Analysis, and Manufacturing, Vol. 9.

    Google Scholar 

  13. Garret, S. E.. 1986. Strategy first: A case in IMS justification.Proceedings of the 2nd ORSA/TIMS Conference on Integrated Manufacturing Systems: Operation Research Models and Applications, K. E. Stecke and R. Suri (eds.), Elsevier Science Publisher, Amsterdam.

    Google Scholar 

  14. Giachetti, R. E., Young, R. E., Roggatz, A., Eversheim, W., Perrone, G.. 1997. A Methodology for Reduction of Imprecision in the Engineering Design Process. European Journal of Operational Research Vol 100.

    Google Scholar 

  15. Goldhar J. and Jelenik M.. 1983. Plan for Economies of Scope. Harvard Business Review, Vol. 61, No. 6.

    Google Scholar 

  16. Hundy B. B. and Hamblin D. J.. 1988. Risk and assessment of investment in new technology. Int. Jour. of Prod. Res., Vol. 26, N. 11.

    Google Scholar 

  17. Inuiguchi M. and Ichihashi H.. 1990. Relative modalities and their use in possibilistic linear programming. Fuzzy Set and Systems, Vol. 35.

    Google Scholar 

  18. Kim, K., Cormier, D., O’Grady, P. and Young, R.. 1995. A System for Design and Concurrent Engineering Under Imprecision. Journal of Intelligent Manufacturing, Vol. 6.

    Google Scholar 

  19. Kosko, B.. 1996. Additive fuzzy systems: from function approximation to learning. Fuzzy Logic and Neural Network Handbook, Edited by C. H. Chen, McGraw-Hill.

    Google Scholar 

  20. Kotha S. and Orne D.. 1989. Generic Manufacturing Strategies: A conceptual synthesis. Strategic Management Journal Vol. 10.

    Google Scholar 

  21. Krinsky I. and Miltenburg J.. 1991. Alternate method for the justification of advanced manufacturing technologies. Int. Jour. of Prod. Res., Vol. 29, N. 5.

    Google Scholar 

  22. Lee E. S. and Li R. J.. 1993. Fuzzy multiple Objective Programming and Compromise Solution with Pareto Optimum. Fuzzy Set and Systems, Vol. 53.

    Google Scholar 

  23. Lo Nigro G., Noto La Diega S., Perrone G.. 1995. Modelli per la Progettazione Strategica dei Sistemi Flessibili di Lavorazione. Procedure di Gestione delle Risorse nei Sistemi Integrati di Produzione, Ed. P.E. Corti and Klinger - Collana MATMIX, Padova.

    Google Scholar 

  24. Mamdani E. H. and Efstathiou H. J.., 1986. Expert Systems and Optimisation in Process Control. Unicorn Series, Technical Press. Aldershot.

    Google Scholar 

  25. Michewicz, Z..1996. Genetic Algorithms + Data Structures = Evolution Programs. Third Springer, New York, New York

    Google Scholar 

  26. Naik B. and Chakravarty A. K.. 1992. Strategic acquisition of new manufacturing technology: a review and research framework. Int. Jour. of Prod. Res., Vol. 30, No. 7.

    Google Scholar 

  27. Navinchandra, D., and Rinderle, J.. 1990. Interval Approaches for Concurrent Evaluation of Design Constraints. Proceedings of Concurrent Product and Process Design, San Francisco, CA, ASME Publication DE-21.

    Google Scholar 

  28. Nelson C. A. and Knight L. B.. 1986. A Mathematical Programming Formulation of Elements of Manufacturing Strategy: IMS Applications. Proceedings of the 2nd ORSA/TIMS Conference on Integrated Manufacturing Systems: Operation Research Models and Applications, K. E. Stecke and R. Suri (eds.), Elsevier Science Publisher, Amsterdam.

    Google Scholar 

  29. Otto, K. N., and Antonsson, E. K.. 1993. The method of Imprecision Compared to Utility Theory for Design Selection Problems. DE-Vol. 53, Proceedings of the fifth Int. Conference on Design Theory and Methodology, ASME, Sept. 19–22, Albuquerque, NM.

    Google Scholar 

  30. Pendlebury A. J.. 1987. Creating a Manufacturing Strategy to suit your Business. Long Range Planning, Vol. 20, No. 6.

    Google Scholar 

  31. Perrone G. 1994. Fuzzy Multiple Criteria Decision Model for the Evaluation of AMS. CIMS Journal, Vol. 7.

    Google Scholar 

  32. Perrone G. and Noto La Diega S. 1996. Strategic IMS Design Under Uncertainty: A Fuzzy Set Theory Based Approach. Int. J. of Prod. Econ., Vol. 46–47.

    Google Scholar 

  33. Perrone, G. and Young R.E.. 1997. The use of Fuzzy Possibilistic Programming in dealing imprecision in the early phases of product design Proceeding of ALTEM III, Fusciano (SA).

    Google Scholar 

  34. Porter M. E.. 1987. Competitive Strategy,The Free Press - New York.

    Google Scholar 

  35. Reusch, B.. 1993. Potentiale der Fuzzy-Technologie in Nordhein-Westfalen. Studie der Fuzzy-Initiative NRW, Ministerium fr Wirtschaft, Mittelstand und Technologie des Landes Nordhein-Westfalen, Dsseldorf.

    Google Scholar 

  36. Skinner W.. 1985. Manufacturing: The formidable Competitive Weapon. John Wiley and sons, New York.

    Google Scholar 

  37. Solberg J.. 1978. Analytical Performance Evaluation for the Design of Integrated Manufacturing Systems. Proceedings of the IEEE Conference on Decision and Control, San Diego CA.

    Google Scholar 

  38. Solberg J. and Nof S.. 1980. Analysis of Flow Control in Alternative Manufacturing Configurations. Journal of Dynamic Systems, Measurement and Control, Vol. 102, No. 141.

    Google Scholar 

  39. Stam A. and Kuula M.. 1991. Selecting a Integrated Manufacturing System using Multiple Criteria Analysis. Int. J. of Prod. Res., Vol. 29, No. 4.

    Google Scholar 

  40. Suri R. and Hildebrant R.. 1984. Modelling Integrated Manufacturing Systems using Mean Flow Analysis. Journal of Manufacturing Systems, Vol. 3, No. 1.

    Google Scholar 

  41. Swamidass P. M.. 1986. Manufacturing Flexibility: Strategic issues. Discussion Paper 305, Graduate School of Business, Indiana University, IN.

    Google Scholar 

  42. Swamidass P. M. and Newell W. T.. 1987. Manufacturing Strategy, Environmental Uncertainty and Performance: a Path Analytical Model. Management Science, Vol. 33, No. 4.

    Google Scholar 

  43. Terceno A., Marquez N., Barbera M. G.. 1994. Funcion de pertenencia del termino amortizativo de un prestamo a interes incierto. Comunication Papers of SIGEF I, Vol. I, Reus.

    Google Scholar 

  44. Trappey, J.-F., Liu, C. R., Chang, T.-C.. 1988. Fuzzy non-linear programming: Theory and application in manufacturing. Int. J. of Prod. Res., Vol. 26.

    Google Scholar 

  45. Wood, K. L., Otto K. N., Antonsson, E. K.. 1992. Engineering design calculations with fuzzy parameters. Fuzzy Sets and Systems, Vol. 52.

    Google Scholar 

  46. Young, R. E., Perrone, G., Eversheim, W., Roggatz, A.. 1995. Fuzzy Constraint Satisfaction for Simultaneous Engineering. Annals of the German Academic Society for Production Engineering, Vol. II, Issue 2.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Perrone, G., La Diega, S.N. (1999). A Simultaneous Approach for IMS Design: a Possibility Based Approach. In: Brandimarte, P., Villa, A. (eds) Modeling Manufacturing Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-03853-6_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-03853-6_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-08483-6

  • Online ISBN: 978-3-662-03853-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics