Aspiration-Led Decision Support Systems: Theory and Methodology

  • A. P. Wierzbicki
Part of the CISM International Centre for Mechanical Sciences book series (CISM, volume 320)


This paper presents a review of the theory and methodology of aspiration-led multi-objective decision support systems as developed during the last decade. After a short historical note on the development of decision analysis and multi-objective optimization, diverse ways of understanding the concept of rationality in decision analysis and support are discussed and the development of aspiration-led decision support systems (ALDSS) is outlined. Foundations of multi-objective optimization theory are reviewed and their relations to reference point optimization, achievement scalarizing functions and aspiration-led decision support are presented, together with their extensions to the problems of dynamics, uncertainty, multi-person decisions. Various aspects of methodology of aspiration-led decision support are discussed along with the description of theoretical results. Some further aspects, such as the phases of decision support, the questions of possible standards of model computerization, the issues of optimization tools, of interactive graphics for decision support, are only outlined. Instead of conclusions, topics for further studies are indicated.


Decision Support System Multiobjective Optimization Positive Cone Decision Situation Multiple Criterion Decision 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Sawaragi, Y., H. Nakayama and T. Tanino: Theory of Multiobjective Optimization. Academic Press, Orlando Fl., 1985.MATHGoogle Scholar
  2. 2.
    Yu, P.L.: Multiple-Criteria Decision Making - Concepts, Techniques and Extensions. Plenum Press, New York and London 1985.CrossRefMATHGoogle Scholar
  3. 3.
    Steuer, R.E.: Multiple Criteria Optimization: Theory, Computation and Application. J. Wiley, New York 1986.Google Scholar
  4. 4.
    Seo, F. and M. Sakawa: Multiple Criteria Decision Analysis in Regional Planning: Concepts, Methods and Applications. D. Reidel Publishing Company, Dordrecht 1988.Google Scholar
  5. 5.
    Keen, P.G.W. and M.S. Scott Morton: Decision Support Systems - an Organizational Perspective. Addison - Wesley, 1978.Google Scholar
  6. 6.
    Bonczek, R.H., C.W. Holsapple and A.B. Whinston: Foundations of Decision Support Systems. Academic Press, New York 1981.Google Scholar
  7. 7.
    Wierzbicki, A.P.: The use of reference objectives in multiobjective optimization. In G. Fandel and T. Gal, eds.: Multiple Criteria Decision Making, Theory and Applications. Springer Verlag, Heidelberg, 1980.Google Scholar
  8. 8.
    Wierzbicki, A.P.: A mathematical basis for satisficing decision making. Mathematical Modeling, Vol. 3 (1982) pp. 391–405.MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Grauer, M., M. Thompson and A.P. Wierzbicki, eds.: Plural Rationality and Interactive Decision Processes. Proceedings, Sopron 1983, Hungary. Springer Verlag, Berlin - Heidelberg, 1984.Google Scholar
  10. 10.
    Dreyfus, S.E.: Beyond rationality. In (9], 1984.Google Scholar
  11. 11.
    Simon, H.: Models of Man. MacMillan, New York 1957.MATHGoogle Scholar
  12. 12.
    Simon, H.: Administrative Behavior. MacMillan, New York 1958.Google Scholar
  13. 13.
    Tietz, H., W. Albers and R. Selten, eds.: Bounded Rational Behavior in Experimental Games and Markets. Proceedings, Bielefeld 1986. Springer Verlag, Berlin - Heidelberg, 1988.Google Scholar
  14. 14.
    Galbraith, J.K.: The New Industrial State. Houghton - Mifflin, Boston 1967.Google Scholar
  15. 15.
    Rappoport, A.: Uses of experimental games. In [9], 1984.Google Scholar
  16. 16.
    Axelrod, R.: The Evolution of Cooperation. Basic Books New York 1985.Google Scholar
  17. 17.
    Charnes, A. and W.W. Cooper: Goal programming and multiple objective optimization. J. Oper. Res. Soc., Vol. 1 (1977) pp. 39–54.MathSciNetCrossRefMATHGoogle Scholar
  18. 18.
    Ignizio, J.P.: Goal programming–a tool for multiobjective analysis. Journal for Operational Research, Vol. 29 (1978) pp. 1109–1119.MATHGoogle Scholar
  19. 19.
    Glushkov, V.M.: Basic principles of automation in organizational management systems (in Russian). Upravlayushcheye Sistemy i Mashiny, Vol. 1 (1972).Google Scholar
  20. 20.
    Pospelov, G.S. and V.A. Irikov: Program-and Goal-Oriented Planning and Management (in Russian). Sovietskoye Radio, Moscow 1976.Google Scholar
  21. 21.
    Umpleby, S.A.: A group process approach to organizational change. In H. Wedde, ed.: Adequate Modeling of Systems. Springer Verlag, Berlin - Heidelberg 1983.Google Scholar
  22. 22.
    Wierzbicki, A.P.: Negotiation and mediation in conflicts, I: The role mathematical approaches and methods (in H. Chestnut et al., eds.: Supplemental Ways to Increase International Stability, Pergamon Press, Oxford 1983), II: Plural rationality and interactive decision processes (in [9] 1984 ).Google Scholar
  23. 23.
    Haimes, Y.Y. and W.A. Hall: Multiobjectives in water resource systems analysis: the surrogate trade-off method. Water Resource Research, Vol. 10 (1974) pp. 615–624.CrossRefGoogle Scholar
  24. 24.
    Larichev, O.I.: Man-machine procedures for decision making. Automation and Remote Control, Vol. 32 (1972) pp. 1973–1983.Google Scholar
  25. 25.
    Fandel, G.: Optimale Entscheidungen bei mehrfacher Zielsetzung. Springer Verlag, Heidelberg 1972.CrossRefGoogle Scholar
  26. 26.
    Keeney, R. and H. Raiffa: Decisions with Multiple Objectives: Preferences and Value Trade-Offs. J. Wiley, New York 1976.Google Scholar
  27. 27.
    Kallio, M., A. Lewandowski and W. Orchard-Hays: An implementation of the reference point approach for multi-objective optimization. WP-80–35, IIASA, Laxenburg 1980.Google Scholar
  28. 28.
    Lewandowski, A. and A.P. Wierzbicki, eds.: Aspiration Based Decision Support Systems. Springer Verlag, Berlin - Heidelberg 1989.MATHGoogle Scholar
  29. 29.
    Grauer, M., A. Lewandowski and L. Schrattenholzer: Usé of the reference level approach for the generation of efficient energy supply strategies. WP-82–19, IIASA, Laxenburg 1982.Google Scholar
  30. 30.
    Grauer, M. and E. Zalai: A reference point approach to nonlinear macroeconomic planning. WP-82–134, IIASA, Laxenburg 1982.Google Scholar
  31. 31.
    Messner, S.: Natural gas trade in Europe and interactive decision analysis. In G. Fandel et al., eds.: Large Scale Modeling and Interactive Decision Analysis. Springer Verlag, Berlin - Heidelberg 1985.Google Scholar
  32. 32.
    Strubegger, M.: An approach for integrated energy - economy decision analysis: the case of Austria.. In G. Fandel et al., eds. (see [31]).Google Scholar
  33. 33.
    Lewandowski, A., S. Johnson and A.P. Wierzbicki: A selection committee decision support system. In Y. Sawaragi, K. Inue and H. Nakayama, eds.: Towards Interactive and Intelligent Decision Support Systems, Springer Verlag, Berlin - Heidelberg 1986.Google Scholar
  34. 34.
    Nakayama, H. and Y. Sawaragi: Satisficing trade-off method for multi-objective programming. In M. Grauer and A.P. Wierzbicki, eds.: Interactive Decision Analysis, Springer Verlag, Berlin - Heidelberg 1983.Google Scholar
  35. 35.
    Nakayama, H.: Sensitivity and trade-off analysis in multiobjective programming. In [39].Google Scholar
  36. 36.
    Steuer, R.E. and E.Y. Choo: an interactive weighted Chebyshev procedure for multiple objective programming. Mathematical Programming Vol. 26 (1983) pp. 326–344.MathSciNetCrossRefMATHGoogle Scholar
  37. 37.
    Korhonen, P. and J. Laakso: Solving a generalized goal programming problem using a visual interactive approach. European Journal of Operational Research, Vol. 26 (1986), pp. 355–363.MathSciNetCrossRefMATHGoogle Scholar
  38. 38.
    Korhonen, P. and J. Wallenius: A careful look at efficiency in multiple objective linear programming. In A.G. Lockett and G. Islei, eds.: Improving Decision Making in Organisations, Proceedings, Manchester 1988. Springer Verlag, Berlin - Heidelberg 1989.Google Scholar
  39. 39.
    Lewandowski, A. and I. Stanchev, eds.: Methodology and Software for Interactive Decision Support. Springer Verlag, Berlin - Heidelberg, 1989MATHGoogle Scholar
  40. 40.
    Kuhn, H.W. and A.W. Tucker: Nonlinear Programming. In Proceedings of Second Berkeley Symposium on Mathematical Statistics and Probability, University of California Press, Berkeley, Cal. (1950), pp. 481–492.Google Scholar
  41. 41.
    Geoffrion, A.M.: Proper efficiency and the theory of vector optimization. Journal of Mathematical Analysis and Applications, Vol 22 (1968), pp. 618–630.MathSciNetCrossRefMATHGoogle Scholar
  42. 42.
    Henig, M.I.: Proper efficiency with respect to cones. Journal of Optimization Theory and Applications, Vol. 36 (1982) pp. 387–407.MathSciNetCrossRefMATHGoogle Scholar
  43. 43.
    Wierzbicki, A.P.: Basic properties of scalarizing functionals for multiobjective optimization. Mathematische Operationsforschung and Statistik, s. Optimization, Vol. 8 (1977) pp. 55–60.MathSciNetGoogle Scholar
  44. 44.
    Wierzbicki, A.P.: On the completeness and constructiveness of parametric characterizations to vector optimization problems. OR-Spektrum, Vol. 8 (1986) pp. 73–87.MathSciNetCrossRefMATHGoogle Scholar
  45. 45.
    Wierzbicki, A.P.: Multiple criteria solutions in noncooperative game theory. Part III: Theoretical foundations. Kyoto Institute of Economic Research, Discussion Paper No. 288, 1990.Google Scholar
  46. 46.
    Salukvadze, M.L.: Vector Valued Optimization Problems in Control Theory. Academic Press, New York 1979.MATHGoogle Scholar
  47. 47.
    Volkovich, V.L.: Multicriteria problems and methods of their solutions (in Ukrainian). In Complex Control Problems, Izdatelstvo Naukova Dumka. Kiev 1969.Google Scholar
  48. 48.
    Salukvadze, M.L.: On the optimization of vector functionals, 1. Programming optimal trajectories, 2. Analytic construction of optimal controllers. Automation and Remote Control, Vol. 31 (1971) No. 7, 8.Google Scholar
  49. 49.
    Yu, P.L. and G. Leitmann: Compromise solutions, domination structures and Salukvadze’s solution. JOTA Vol. 13 (1974), pp. 362–378.MathSciNetCrossRefMATHGoogle Scholar
  50. 50.
    Zeleny, M.: Compromise programming. In J.L. Cochrane and M. Zeleny, eds.: Multiple Criteria Decision Making. Univ. of South Carolina Press, Columbia S. Carolina 1973.Google Scholar
  51. 51.
    Zeleny, M.: Multiple Criteria Decision Making. McGraw Hill, New York 1982.MATHGoogle Scholar
  52. 52.
    Geoffrion, A.M.: Duality in nonlinear programming:,a simplified application-oriented development. SIAM Review Vol. 13 (1971) pp. 1–37.MathSciNetCrossRefMATHGoogle Scholar
  53. 53.
    Benson, H.P. and T.L. Morin: THe vector maximization problem: proper efficiency and stability.SIAM Journal on Applied Mathematics, Vol. 32 (1977) pp. 64–72.MathSciNetMATHGoogle Scholar
  54. 54.
    Wierzbicki, A.P.: Penalty methods in solving optimization problems with vector performance criteria. Proceedings of the Vi-th IFAC World Congress, Cambridge Mass. 1975.Google Scholar
  55. 55.
    Wierzbicki, A.P.: Models and Sensitivity of Control Systems. Elsevier, Amsterdam 1984.MATHGoogle Scholar
  56. 56.
    Wierzbicki, A.P. Dynamics aspects of multi-objective optimization. Proceedings of Yalta Conference on Vector Optimization, Springer Verlag, Heidelberg - Berlin 1990.Google Scholar
  57. 57.
    Li, D. and Y.Y. Haimes: The envelope approach for multiobjective optimization problems. IEEE-SMC, Vol. 17 (1987) pp. 1026–1038.MathSciNetGoogle Scholar
  58. 58.
    Li, D. and Y.Y. Haimes: Multiobjective dynamic programming - the state of the art. In A.G. Lockett and G. Islei, eds. (see (38]).Google Scholar
  59. 59.
    Wets, R.J.B.: Stochastic programming: solution schemes and approximation techniques. In A. Bachem et al., eds.: Mathematical Programming: The State of the Art. Springer Verlag, Berlin - Heidelberg 1983.Google Scholar
  60. 60.
    Ermolev, Yu.M. and R.J.B. Wets: Numerical Methods in Stochastic Programming. Springer Verlag, Berlin - Heidelberg 1987.Google Scholar
  61. 61.
    Ruszczynski, A.: Modern techniques for linear dynamic and stochastic programs. In [28].Google Scholar
  62. 62.
    Kurzhanski, A.B.: Inverse problems in multiobjective stochastic optimization. In Y. Sawaragi et al., eds. (see [33]).Google Scholar
  63. 63.
    Feldbaum, A.A.: Foundations of the Theory of Optimal Control Systems (in Russian). Nauka, Moscow 1962.Google Scholar
  64. 64.
    Michalevich, M.V.: Stochastic approaches to interactive multicriteria optimization problems. WP-86–10, IIASA, Laxenburg 1986.Google Scholar
  65. 65.
    Roth, A.E.: Axiomatic Models of Bargaining. Springer Verlag, Berlin - Heidelberg 1979.CrossRefMATHGoogle Scholar
  66. 66.
    Wierzbicki, A.P.: Multiobjective decision support for simulated gaming. In A.G. Lockett and G. Islei, eds. (see [38]).Google Scholar
  67. 67.
    Bronisz, P., L. Krus and A.P. Wierzbicki: Towards interactive solutions in a bargaining problem. In [28].Google Scholar
  68. 68.
    Cooke, S. and N. Slack: Making Management Decisions. Prentice - Hall, Englewood Cliffs 1984.Google Scholar
  69. 69.
    Kreglewski, T., J. Paczynski, J. Granat and A.P. Wierzbicki: IAC-DIDAS-N - a dynamic interactive decision analysis and support system for multi-criteria analysis of nonlinear models. In [28].Google Scholar
  70. 70.
    Makowski, M. and J.P. Sosnowski: Mathematical programming package HYBRID. In [28].Google Scholar

Copyright information

© Springer-Verlag Wien 1991

Authors and Affiliations

  • A. P. Wierzbicki
    • 1
  1. 1.Warsaw University of TechnologyWarsawPoland

Personalised recommendations