A Fuzzy-GA Decision Support System for Enhancing Postponement Strategies in Supply Chain Management

  • Cassandra X. H. Tang
  • Henry C. W. Lau
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5361)


This paper aims to propose a knowledge-based Fuzzy - GA Decision Support System with performance metrics for better measuring postponement strategies. The Fuzzy - GA approach mainly consists of two stages: knowledge representation and knowledge assimilation. The relevant knowledge of deciding what type of postponement strategies to adopt is encoded as a string with a fuzzy rule set and the corresponding membership functions. The historical data on performance measures forming a combined string is used as the initial population for the knowledge assimilation stage afterwards. GA is then further incorporated to provide an optimal or nearly optimal fuzzy set and membership functions for related performance measures. The originality of this research is that the proposed system is equipped with the ability of assessing the loss caused by discrepancy away from the different supply chain parties, and therefore enabling the identification of the best set of decision variables.


Supply chain management Hybrid optimisation algorithms 


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  1. 1.
    Ballou, R.H.: Business Logistics Management: Planning, Organising, and Controlling the Supply Chain, 4th edn. Prentice-Hall International, Englewood Cliffs (1999)Google Scholar
  2. 2.
    Lee, H.L.: Design for Supply Chain Management: Concepts and Examples. In: Elwood, S., Buffa, R.K. (eds.) Perspectives in Operations Management: Essays in Honor of Elwood, ch. 3, pp. 45–66. Kluwer Academic Publishers, Boston (1993)CrossRefGoogle Scholar
  3. 3.
    Whang, S., Lee, H.L.: Value of Postponement. In: Product Variety Management Research Advances, ch. 4, pp. 65–84. Kluwer Academic Publishers, Boston (1999)Google Scholar
  4. 4.
    Bitran, G.R., Haas, E.A., Matsuo, H.: Production Planning of Style Goods with High Setup Costs and Forecast Revisions. Operations Research 34(2), 226–236 (1986)CrossRefzbMATHGoogle Scholar
  5. 5.
    Fisher, M., Raman, A.: Reducing the Cost of Demand Uncertainty Through Accurate Response to Early Sales. Operations Research 44(1), 87–99 (1996)CrossRefzbMATHGoogle Scholar
  6. 6.
    Alderson, W.: Marketing Efficiency and the Principle of Postponement, Cost and Profit Outlook (3) (1950)Google Scholar
  7. 7.
    Bowersox, D.J., Closs, D.J.: Logistical Management: the Integrated Supply Chain Process. Macmillan, New York (1996)Google Scholar
  8. 8.
    Lee, H.L., Tang, C.S.: Modeling The Costs And Benefits of Delay Product Differentiation. Management Science 43(1), 40–54 (1997)CrossRefzbMATHGoogle Scholar
  9. 9.
    Harvard Business School, Benetton (A) and (B), Harvard Teaching Case 9-685-014, Cambridge, MA (1986)Google Scholar
  10. 10.
    Iacocca Institute, 21st Century Manufacturing Enterprise Strategies, Lehigh University, Bethlehem, PA (1991)Google Scholar
  11. 11.
    Van Hoek, R.I.: The discovery of postponement: a literature review and directions for research. Journal of Operations Management 19(2), 161–184 (2000)CrossRefGoogle Scholar
  12. 12.
    Christopher, M.: The agile supply chain: competing in volatile markets. Industrial Marketing Management 29(1), 37–44 (2000)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Lee, H.L., Whang, S.: Winning the last mile of e-commerce. MIT Sloan Management Review 42(4), 54–62 (2001)Google Scholar
  14. 14.
    Yang, B., Burns, N.D., Backhouse, C.J.: Implications of postponement for the supply chain. International Journal of Production Research 41(9), 2075–2090 (2003)CrossRefGoogle Scholar
  15. 15.
    Agrawal, M.K., Pak, M.H.: Getting smart about supply chain management. The McKinsey Quarterly 2, 22–25 (2001)Google Scholar
  16. 16.
    Bowersox, D.J., Closs, D.J.: Logistical Management: the Integrated Supply Chain Process. Macmillan, New York (1996)Google Scholar
  17. 17.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. MIT Press, Cambridge (1992)Google Scholar
  18. 18.
    Gen, M., Cheng, R.: Genetic algorithms and engineering optimization. Wiley, New York (2000)Google Scholar
  19. 19.
    Al-Kuzee, J., Matsuura, T., Goodyear, A., Nolle, L., Hopgood, A.A., Picton, P.D., Braithwaite, N.S.J.: Optimization of plasma etch processes using evolutionary search methods with in situ diagnostics. Plasma Sources Science Technology 13(4), 612–622 (2004)CrossRefGoogle Scholar
  20. 20.
    Santos, C.A., Spim, J.A., Ierardi, M.C.F., Garcia, A.: The use of artificial intelligence technique for the optimisation of process parameters used in the continuous casting of steel. Applied Mathematical Modelling 26(11), 1077–1092 (2002)CrossRefzbMATHGoogle Scholar
  21. 21.
    Li, T.S., Su, C.T., Chiang, T.L.: Applying robust multi-response quality engineering for parameter selection using a novel neural–genetic algorithm. Computers in Industry 50(1), 113–122 (2003)CrossRefGoogle Scholar
  22. 22.
    Milfelner, M., Kopac, J., Cus, F., Zuperl, U.: Genetic equation for the cutting force in ball-end milling. Journal of Materials Processing Technology 164-165, 1554–1560 (2005)CrossRefGoogle Scholar
  23. 23.
    Leung, R.W.K., Lau, H.C.W., Kwong, C.K.: An expert system to support the optimization of ion plating process: an OLAP-based fuzzy-cum-GA approach. Expert Systems with Applications 25(3), 313–330 (2003)CrossRefGoogle Scholar
  24. 24.
    Leung, B.P.K., Spiring, F.A.: The inverted beta loss function: properties and applications. IIE Transactions 34(12), 1101–1109 (2002)Google Scholar
  25. 25.
    Taguchi, G.: Introduction to Quality engineering: Designing Quality into Products and processes. Kraus, White Plains, NY (1986)Google Scholar
  26. 26.
    Fatikow, S., Rembold, U.: Microsystem Technology and Microrobotics. Springer, Heidelberg (1997)CrossRefzbMATHGoogle Scholar
  27. 27.
    van Hoek, R.I.: The rediscovery of postponement a literature review and directions for research. Journal of Operations Management 19(2), 161–184 (2001)CrossRefGoogle Scholar
  28. 28.
    Yang, B., Burns, N.D., Backhouse, C.J.: The application of postponement in industry. IEEE Transactions on Engineering Management 52(2), 238–248 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Cassandra X. H. Tang
    • 1
  • Henry C. W. Lau
    • 1
  1. 1.Dept. of Industrial and Systems EngineeringThe Hong Kong Polytechnic University HunghomKowloonHong Kong

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