Mathematical Modelling of Decision Making Support Systems Using Fuzzy Cognitive Maps

  • Peter P. Groumpos
  • Ioannis E. Karagiannis
Part of the Studies in Computational Intelligence book series (SCI, volume 444)

Abstract

This chapter critically analyses the nature and state of Decision Support Systems (DSS) theories, research and applications. A thorough and extensive historical review of DSS is provided which focuses on the evolution of a number of sub-groupings of research and practice: personal decision support systems, group support systems, negotiation support systems, intelligent decision support systems, knowledge management- based DSS, executive information systems/ business intelligence, and data warehousing. The need for new DSS methodologies and tools is investigated. The DSS area has remained vital as technology has evolved and our understanding of Decision-Making process has deepened. DSS over the last twenty years has contributed both breadth and depth to DSS research. The challenge now is to make sense of it in ‘’Decision Making” by planning it in understanding context and by searching new ways to utilize other advanced methodologies. The possibility of using Fuzzy Logic, Fuzzy Cognitive Maps and Intelligent Control in DSS is reviewed and analyzed. A new generic method for DSS is proposed, the Decision Making Support System (DMSS). Basic components of the new generic method are provided and fully analyzed. Case studies are given showing the usefulness of the proposed method.

Keywords

Decision Support Systems Intelligent Control Fuzzy Systems Decision Making Support Systems Fuzzy Cognitive Maps 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Stylios, C.D., Groumpos, P.P., Georgopoulos, V.C.: An fuzzy cognitive maps approach to process control systems. Journal of Advanced Computational Intelligence 3, 409–417 (1999)Google Scholar
  2. 2.
    Stylios, C.D., Groumpos, P.P.: Modeling complex systems using fuzzy cognitive maps. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 34, 155–162 (2004)CrossRefGoogle Scholar
  3. 3.
    D’alche-Buc, F., Zwierski, D., Nadal, J.: Trio learning: a new strategy for building hybrid neural trees. Neural Syst. 5(4), 255–274 (1994)Google Scholar
  4. 4.
    Janssens, D., Wets, G., Brijs, T., Vanhoof, K., Arentze, T., Timmersmans, H.: Intergrating Bayesian networks and decision trees in a sequential rule-based transportation model. Europ. J. Operat. Research (2005)Google Scholar
  5. 5.
    Podgorelec, V., Kokol, P., Tiglic, S.B., Rozman, I.: Decision Trees: An overview and their Use in Medicine. Journal of Medical Systems 5 (October 2002)Google Scholar
  6. 6.
    Papageorgiou, E., Stylios, C., Groumpos, P.P.: An Intergrated Two-Level Hierarchical Decision Making System based on Fuzzy Cognitive Maps (FCMs). IEEE Trans. Biomed. Engin. 50(12), 1326–1339 (2003)CrossRefGoogle Scholar
  7. 7.
    Papageorgiou, E.I., Groumpos, P.P.: A weight adaption method for fine-tuning Fuzzy Cognitive Map casual links. Soft Computing Journal 9, 846–857 (2005)MATHCrossRefGoogle Scholar
  8. 8.
    Papageorgiou, E., Stylios, C., Groumpos, P.P.: Unsupervised learning techniques for fine-tuning Fuzzy Cognitive Map casual links. Intern. Journal of Human-Computer Studies 64, 727–743 (2006)CrossRefGoogle Scholar
  9. 9.
    Groumpos, P.P., Stylios, C.D.: Modeling supervisory control systems using fuzzy cognitive maps. Chaos Solit Fract. 11(3), 329–336 (2000)MathSciNetMATHCrossRefGoogle Scholar
  10. 10.
    Papageorgiou, E.I., Stylios, C.D., Groumpos, P.P.: Active Hebbian learning algorithm to train fuzzy cognitive maps. International Journal of Approximate Reasoning 37, 219–249 (2004)MathSciNetMATHCrossRefGoogle Scholar
  11. 11.
    Hamrouni, N., Jraidi, M., Cherif, A.: Solar radiation and ambient temperature effects on the performances of a PV pumping system. Revue des Energies Renouvelables 11(1), 95–106 (2008)Google Scholar
  12. 12.
    Anderson, P.M., Bose, A.: Stability Simulation of Wind Turbine Systems. IEEE Trans. on Power Apparatus and Systems PAS-102(12), 3791–3795 (1983)Google Scholar
  13. 13.
    Nehrir, M.H., Lameres, B.J., Venkataramanan, G., Gerez, V., Alvarado, L.A.: An approach to evaluate the general performance of stand-alone wind/photovoltaic generating systems 15(4), 433–439 (2000)Google Scholar
  14. 14.
    Yang, H., Zhoo, W., Lu, L., Fang, Z.: Optimal sizing method for stand-alone hybrid solar-wind system with LPSP technology by using genetic algorithm 82(4), 354–367 (2007)Google Scholar
  15. 15.
    Kosko, B.: Fuzzy cognitive maps. International Journal of Man–Machine Studies 24, 65–75 (1986)MATHCrossRefGoogle Scholar
  16. 16.
    Hyvarinen, A., Oja, E.: Independent component analysis by general nonlinear Hebbian-like learning rules. Signal Processing 64(3), 301–313 (1998)CrossRefGoogle Scholar
  17. 17.
    Karhunen, J., Joutsensalo, J.: Nonlinear Hebbian algorithm for sinusoidal frequency estimation. In: Aleksander, J., Taylor, J.G. (eds.) Artificial Neural Networks, vol. 2, pp. 1099–1102. North-Holland, Amsterdam (1992)Google Scholar
  18. 18.
    Harris, A.J.: Lifeline: Call Centers and Crisis Management. Risk Management 55(5), 42–55 (2008)Google Scholar
  19. 19.
    Moynihan, D.: Learning under uncertainty: Networks in Crisis Management. Public Administration Review 68(2), 350–365 (2008)CrossRefGoogle Scholar
  20. 20.
    Bolloju, N., Khalifa, M., Turban, E.: Intergrating knowledge management into enterprise environments for the next generation decision support. Decision Support Systems: Directions for the Next Decade 33(2), 163–176 (2002)CrossRefGoogle Scholar
  21. 21.
    Lam, W.: Investigating success factors in enterprise application: A case driven analysis. European Journal of Information Systems 14, 175–187 (2005)CrossRefGoogle Scholar
  22. 22.
    Al-Mashari, M., Al-Mudimigh, A., Zairi, M.: Enterprise resourse planning: A taxonomy of critical factors. European Journal of Operational Research 146(2), 352–364 (2003)MATHCrossRefGoogle Scholar
  23. 23.
    Caves, R.: Multinational Enterprise and Economic Analysis, 3rd edn. (2007)Google Scholar
  24. 24.
    Zhiyun, L.: Banking Structure and the Small-Medium-Sized Enterprise Financing. Economic Research Journal (2002)Google Scholar
  25. 25.
    Mamdani, E., Gaines, B.: Fuzzy Reasoning and its applications. Publ. Academic Press, London (1981)MATHGoogle Scholar
  26. 26.
    Mamdani, E.H.: Application on Fuzzy Logic to approximate reasoning using linguistic synthesis. IEEE Trans. on computers C26. Dic., 1182–1191 (1977)Google Scholar
  27. 27.
    Wang, L.: Fuzzy systems are universal approximators. In: Proc. of Int. Conf. on Fuzzy Engineering, pp. 471–496 (1992)Google Scholar
  28. 28.
    McNeil, D., Freiberger, P.: Fuzzy Logic: The Revolutionary Computer Technology That is Changing Our World (April 1994)Google Scholar
  29. 29.
    Zadeh, L.A.: Making computer think like people. IEEE Spectrum 21, 26–32 (1984)Google Scholar
  30. 30.
    Haack, S.: Do we need fuzzy logic? Int. Jrnl. of Man-Mach. Stud. 11, 437–445 (1979)MathSciNetMATHCrossRefGoogle Scholar
  31. 31.
    Runkler, T.A., Glesner, M.: Defuzzification and ranking in the context of membership value semantics, rule modality, and measurements theory. In: European Congress on Fuzzy and Intelligent Technologies, Aachen (September 1994)Google Scholar
  32. 32.
    Pfluger, N., Yen, J., Langari, R.: A defuzzyfication strategy for a fuzzy logic controller employing prohibitive information in command formulation. In: Proceedings of IEEE International Conference on Fuzzy Systems, San Diego, pp. 717–723 (1992)Google Scholar
  33. 33.
    Runkler, T.A.: Selection of appropriate defuzzyfication methods using application specific properties. IEEE Transaction on Fuzzy Systems 5(1), 72–79 (1997)CrossRefGoogle Scholar
  34. 34.
    Saade, J.J.: A unifying approach to defuzzification and comparison of the outputs of fuzzy controller. IEEE Trans. Fuzzy Syst. 4, 227–237 (1996)CrossRefGoogle Scholar
  35. 35.
    Jang, J.-S.R.: Fuzzy Inference Systems. ch. 4, 73–91 (1997)Google Scholar
  36. 36.
    Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Intenational Journal of Man-Machine Studies 7(1), 1–13 (1975)MATHCrossRefGoogle Scholar
  37. 37.
    Albus, J.S.: Outline for a Theory of Intelligence. IEEE Transactions on Systems, Man and Cybernetics 21(3), 432–509 (1991)MathSciNetCrossRefGoogle Scholar
  38. 38.
    Saridis, G.N., Valavanis, K.P.: Analytical Design of Intelligent Machines. Automatica 24(2), 123–133 (1988)MATHCrossRefGoogle Scholar
  39. 39.
    White, D.A., Sofge, D.A. (eds.): Handbook of Intelligent Control Neural, Fuzzy, and Adaptive Approaches. Van Nostrand Reinhold, New York (1992)Google Scholar
  40. 40.
    Gupta, M.M., Sinha, N.K. (eds.): Intelligent Control: Theory and Practice. IEEE Press, Piscataway (1994)Google Scholar
  41. 41.
    Glykas, M.: Fuzzy Cognitive Maps: Advances in Theory, Methodologies, Tools and Applications. Springer, Heidelberg (2010)MATHCrossRefGoogle Scholar
  42. 42.
    Keen, P.G., Morton, S., Michael, S.: Decision Support Systems: An organizational perspective. Addison-Wesley Pub. Co., Reading (1978)Google Scholar
  43. 43.
    Keen, P.G.: Decision Support Systems: the next decade. Decision Support Systems 3(3), 253–265 (1987)CrossRefGoogle Scholar
  44. 44.
    Keen, P.G.W., Scott Morton, M.S.: Decision support systems: an organizational perspective. Addison-Wesley (1978)Google Scholar
  45. 45.
    Ackoff, R.L.: Management Misinformation Systems. Management Science 14(4), 147–157 (1967)CrossRefGoogle Scholar
  46. 46.
    Mintzberg, H.: Making Management Information Useful. Management Review 64(5), 34–38 (1975)Google Scholar
  47. 47.
    Gorry, G.A., Scott-Morton, M.S.: A framework for management information systems. Sloan Management Review 13(1), 50–70 (1971)Google Scholar
  48. 48.
    Sprague, R.H., Watson, H.J.: Bit by Bit: Toward Decision Support Systems. California Management Review 22(1), 60–68 (1979)Google Scholar
  49. 49.
    Gerrity Jr, T.P.: Design of Man-Machine Decision Systems: An Application to Port- folio Management. Sloan Management Review 12(2), 59–75 (1971)Google Scholar
  50. 50.
    Sprague, J. R.H., Carlson, E.D.: Building effective decision support systems. Prentice-Hall, Inc., Englewood Cliffs (1982)Google Scholar
  51. 51.
    McCosh, A.M., Scott Morton, M.S.: Management decision support systems. Wiley, New York (1978)Google Scholar
  52. 52.
    Scott Morton, M.S.: Management Decision Systems. Harvard Business School Press, Boston (1971)Google Scholar
  53. 53.
    Alter, S.L.: Decision support systems: Current practice and continuing challenges. Addison-Wesley, Phillipines (1980)Google Scholar
  54. 54.
    Finlay, P.: Introducing Decision Support Systems, 2nd edn. NCC/Blackwell, Malden (1994)Google Scholar
  55. 55.
    Turban, E.: Decision Support Systems and Expert Systems. Prentice Hall (1995)Google Scholar
  56. 56.
    Schroff, A.: An Approach to User Oriented Decision Support Systems, Inaugural-Dissertation Nr. 1208, Druckerei Horn, Bruchsal (1998)Google Scholar
  57. 57.
    Power, D.J.: “What is a DSS?” originally published in DSStar. The On-Line Executive Journal for Data Intensive Decision Support 1(3) (1997)Google Scholar
  58. 58.
    Arnott, D.: Decision support systems evolution: Framework, case study and research agenda. European Journal of Information Systems 13(4), 247–259 (2004)CrossRefGoogle Scholar
  59. 59.
    Bidgoli, H.: Intelligent Management Support Systems, Greenwood, Westport CT (1998)Google Scholar
  60. 60.
    Doukidis, G.I., Land, F., Miller, G.: Knowledge Based Management Support Systems. Ellis Horwood, Chichester (1989)Google Scholar
  61. 61.
    Turban, E., Aronson, J.E., Liang, T.-P.: Decision Support Systems and Intelligent Systems, 7th edn. Pearson Education, Upper Saddle River (2005)Google Scholar
  62. 62.
    Fitzgerald, G.: Executive information systems and their development in the U.K.: A research study. International Information Systems 1(2), 1–35 (1992)MathSciNetGoogle Scholar
  63. 63.
    Rockart, J.F.: Chief executives define their own data needs. Harvard Business Review 57, 81–93 (1979)Google Scholar
  64. 64.
    Kaplan, R.S., Norton, D.P.: The Balanced Scorecard: Translating Strategy into Action. Harvard Business School Press, Cambridge (1996)Google Scholar
  65. 65.
    Inmon, W., Hackathorn, R.: Using the Data Warehouse. John Wiley and Sons, New York (1994)Google Scholar
  66. 66.
    Kimball, R., Reeves, L., Ross, M., Thornwaite, W.: The Data Warehousing Lifecycle Toolkit. John Wiley and Sons, Chichester (1998)Google Scholar
  67. 67.
    Kimball, R.: The Data Warehousing Toolkit. John Wiley and Sons, Chichester (1996)Google Scholar
  68. 68.
    Keen, P.G.W.: Let’s focus on action not information: Information is a misleading and damaging IS term. Computerworld 31(46) (1997)Google Scholar
  69. 69.
    Rockart, J.F., DeLong, D.W.: Executive Support Systems: The Emergence of Top Management Computer Use. Dow Jones-Irwin, Illinois (1988)Google Scholar
  70. 70.
    Codd, E.F., Codd, S.B., Salley, C.T.: Providing on-line analytical processing (OLAP) touser-analysts: An IT mandate. E.F. Codd and Associates (1993) (unpublished manuscript)Google Scholar
  71. 71.
    Simon, H.: Administrative Behavior. Free Press, GlencoeGoogle Scholar
  72. 72.
    Meador, C.L., Ness, D.N.: Decision support systems: An approach to corporate planning. Sloan Management Review 15(2), 51–68 (1974)Google Scholar
  73. 73.
    Ness, D.N.: Decision Support Systems: Theories of Design. Presented at the Wharton Office of Naval Research Conference on Decision Support Systems, Philadelphia, Pennsylvania, November 4-7 (1975)Google Scholar
  74. 74.
    Courbon, J.C., Grajew, J., Tolovi, J.: Design and Implementation of Interactive Decision Support Systems: An Evolutive Approach, Technical Report, Institute d’Administration des Enterprises, Grenoble, France (1978)Google Scholar
  75. 75.
    Keen, P.G.W.: Adaptive Design for DSS. Database 12(1-2), 15–25 (1980)Google Scholar
  76. 76.
    Silver, M.S.: Systems that Support Decision Makers: Description and Analysis. John Wiley and Sons, New York (1991)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Peter P. Groumpos
    • 1
  • Ioannis E. Karagiannis
    • 1
  1. 1.Laboratory for Automation and Robotics Department of Electrical and Computer EngineeringUniversity of PatrasRioGreece

Personalised recommendations