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An Interval Type-2 Fuzzy Multiple Echelon Supply Chain Model

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Research and Development in Intelligent Systems XXVI

Abstract

Planning resources for a supply chain is a major factor determining its success or failure. In this paper we build on previous work introducing an Interval Type-2 Fuzzy Logic model of a multiple echelon supply chain. It is believed that the additional degree of uncertainty provided by Interval Type-2 Fuzzy Logic will allow for better representation of the uncertainty and vagueness present in resource planning models. First, the subject of Supply Chain Management is introduced, then some background is given on related work using Type-1 Fuzzy Logic. A description of the Interval Type-2 Fuzzy model is given, and a test scenario detailed. A Genetic Algorithm uses the model to search for a near-optimal plan for the scenario. A discussion of the results follows, along with conclusions and details of intended further work.

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References

  1. R.A. Aliev, B. Fazlollahi, B.G. Guirimov, and R. R. Aliev. Fuzzy-genetic approach to aggregate production-distribution planning in supply chain management. Information Sciences, 177:4241–4255, 2007.

    Article  MATH  Google Scholar 

  2. S. Coupland and R. I. John. Geometric type-1 and type-2 fuzzy logic systems. IEEE Transactions on Fuzzy Systems, 15(1):3–15, February 2007.

    Article  Google Scholar 

  3. D. Dubois and H. Prade. Fuzzy Sets and Systems: Theory and Applications. Academic Press, 1980.

    Google Scholar 

  4. C. Fayad and S. Petrovic. A fuzzy genetic algorithm for real-world job shop scheduling. In Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence. Bari, Italy., pages 524–533, 2005.

    Google Scholar 

  5. H.A. Hagras. A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots. IEEE Transactions on Fuzzy Systems, 12(4):524–539, August 2004.

    Article  Google Scholar 

  6. J.H. Holland. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. Ann Arbor: University of Michigan, 1975.

    Google Scholar 

  7. R.I. John and S. Coupland. Type-2 fuzzy logic a historical view. IEEE Computational Intelligence Magazine, 2(1):57–62, February 2007.

    Article  Google Scholar 

  8. N. Karnik and J. Mendel. Applications of type-2 fuzzy logic systems to forecasting of timeseries. Information Sciences, 120:89–111, 1999.

    Article  MATH  Google Scholar 

  9. N. Karnik and J. Mendel. Centroid of a type-2 fuzzy set. Information Sciences, 132:195–220, 2001.

    Article  MATH  MathSciNet  Google Scholar 

  10. G.J. Klir and T.A. Folger. Fuzzy Sets, Uncertainty and Information. Prentice Hall, 1988.

    Google Scholar 

  11. J. M. Mendel. Advances in type-2 fuzzy sets and systems. Information Sciences, 177(1):84–110, January 2007.

    Article  MATH  MathSciNet  Google Scholar 

  12. J.M. Mendel and R.I. John. Type-2 fuzzy sets made simple. IEEE Transactions on Fuzzy Systems, 10(2):117–127, April 2002.

    Article  Google Scholar 

  13. J.M. Mendel, R.I. John, and F. Liu. Interval type-2 fuzzy logic systems made simple. IEEE Transactions on Fuzzy Systems, 14(6):808–821, December 2006.

    Article  Google Scholar 

  14. S.M. Miller, V. Popova, R. John, and M. Gongora. Improving resource planning with soft computing techniques. In Proceedings of UKCI 2008, De Montfort University, Leicester, UK., pages 37–42, September 2008. available at: http://www.cci.dmu.ac.uk/preprintPDF/SimonUKCI(2).pdf.

  15. S.M. Miller, V. Popova, R. John, and M. Gongora. An interval type-2 fuzzy distribution network. In Proceedings of 2009 IFSA World Congress/EUSFLAT Conference, pages 697–702, Lisbon, Portugal, July 2009.

    Google Scholar 

  16. D. Petrovic, R. Roy, and R. Petrovic. Supply chain modelling using fuzzy sets. International Journal of Production Economics, 59:443–453, 1999.

    Article  Google Scholar 

  17. D. Petrovic, Y. Xie, K. Burnham, and R. Petrovic. Coordinated control of distribution supply chains in the presence of fuzzy customer demand. European Journal of Operational Research, 185:146–158, 2008.

    Article  MATH  MathSciNet  Google Scholar 

  18. M. Sakawa and T.Mori. An efficient genetic algorithm for job-shop scheduling problems with fuzzy processing time and fuzzy duedate. Computers & Industrial Engineering, 36:325–341, 1999.

    Article  Google Scholar 

  19. D.J. Thomas and P.M. Griffin. Coordinated supply chain management. European Journal of Operational Research, 94(1):1–15, October 1996.

    Article  MATH  Google Scholar 

  20. J. Wang and Y-F. Shu. Fuzzy decision modelling for supply chain management. Fuzzy Sets and Systems, 150(1):107–127, 2005.

    Article  MATH  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Centre for Computational Intelligence, De Montfort University, Leicester, UK

    Simon Miller & Robert John

Authors
  1. Simon Miller
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  2. Robert John
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Corresponding author

Correspondence to Simon Miller .

Editor information

Editors and Affiliations

  1. Dept. Computer Science and, University of Portsmouth, Lion Terrace, Portsmouth, PO1 3HE, United Kingdom

    Max Bramer

  2. Stratum Management Ltd., Southbrook Place 11, Micheldever, Hants., SO21 3DE, United Kingdom

    Richard Ellis

  3. School of Computing &, University of Greenwich, Park Row 30, London, SE10 9LS, United Kingdom

    Miltos Petridis

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© 2010 Springer-Verlag London

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Miller, S., John, R. (2010). An Interval Type-2 Fuzzy Multiple Echelon Supply Chain Model. In: Bramer, M., Ellis, R., Petridis, M. (eds) Research and Development in Intelligent Systems XXVI. Springer, London. https://doi.org/10.1007/978-1-84882-983-1_32

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  • DOI: https://doi.org/10.1007/978-1-84882-983-1_32

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84882-982-4

  • Online ISBN: 978-1-84882-983-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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