Determining Multiple Attribute Weights Consistent with Pairwise Preference Orders (original) (raw)

Abstract

This paper presents a method for determining multiple attribute weights when pairwise comparison judgments on alternatives are specified and attribute consequences are captured in imprecise ways. A decision-maker or expert can express holistic pairwise comparisons on alternatives from his/her domain knowledge and decision alternatives are characterized by some tangible or possibly some intangible multiple attributes of which consequences can be represented by imprecise information. In this paper, attribute weights are to be estimated in the direction of minimizing the amount of violations and thus to be as consistent as possible with a decision-maker’s ordered pairs. Multiple attribute weights that were determined with pairwise judgments on a subset of alternatives can be used to prioritize the other remaining alternatives.

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References

  1. Borcherding, K., von Winterfeldt, D.: The effect of varying value trees on multiattribute evaluations. Acta Psychologica 68, 153–170 (1988)
    Article Google Scholar
  2. Shoemaker, P.J.H., Waid, C.D.: An experimental comparison of different approaches to determining weights in additive utility models. Management Sci. 28, 182–196 (1982)
    Article Google Scholar
  3. Stillwell, W.G., Seaver, D.A., Edwards, W.: A comparison of weight approximation techniques in multiattribute utility decision making. Org. Behav. Hum. Proc. 28, 62–77 (1981)
    Article Google Scholar
  4. Barron, F.H., Barrett, B.E.: Decision quality using ranked attribute weights. Management Sci. 42, 1515–1523 (1996)
    Article MATH Google Scholar
  5. Srinivasan, V., Shocker, A.D.: Linear programming techniques for multidimensional analysis of preferences. Psychometrika 38, 337–369 (1973)
    Article MATH MathSciNet Google Scholar
  6. Horsky, D., Rao, M.R.: Estimation of attribute weights from preference comparisons. Management Sci. 30, 801–822 (1984)
    Article MATH MathSciNet Google Scholar
  7. Pekelman, D., Sen, S.K.: Mathematical programming models for the determination of attributes weights. Management Sci 20, 1217–1229 (1974)
    Article MATH Google Scholar
  8. Jacquet-Lagreze, E., Siskos, J.: Assessing a set of additive utility functions for multicriteria decision-making, the UTA method. Eur. J. Oper. Res. 10, 151–164 (1982)
    Article MATH Google Scholar
  9. White, C.C., Sage, A.P., Dozono, S.: A model of multiattribute decisionmaking and trade-off weight determination under uncertainty. IEEE Trans. Syst. Man Cybernet. 14, 223–229 (1984)
    MathSciNet Google Scholar
  10. Zopounidis, C., Dimitras, A.I.: Multicriteria Decision Aid Methods for the Prediction of Business Failure. Kluwer Academic Publishers, Dordrecht (1998)
    MATH Google Scholar
  11. Weber, M.: Decision making with incomplete information. Eur. J. Oper. Res. 28, 44–57 (1987)
    Article MATH Google Scholar
  12. Sage, A.P., White, C.C.: ARIADNE: A Knowledge-based interactive system for planning and decision support. IEEE Trans. Syst. Man Cybernet. 14, 35–47 (1984)
    MathSciNet Google Scholar
  13. Park, K.S., Kim, S.H.: Tools for interactive multiattribute decision making with incompletely identified information. Eur. J. Oper. Res. 98, 111–123 (1997)
    Article MATH Google Scholar
  14. Ahn, B.S., Park, K.S., Han, C.H., Kim, J.K.: Multi-attribute decision aid under hierarchical structure and incomplete information. Eur. J. Oper. Res. 125, 431–439 (2000)
    Article MATH Google Scholar
  15. Mustafi, C.K., Xavier, M.J.: Mixed-integer linear programming formulation of a multi-attribute threshold model of choice. J. Opl Res Soc. 36, 935–942 (1985)
    MATH Google Scholar
  16. Oral, M., Kettani, O.: Modelling the process of multiattribute choice. J. Opl. Res. Soc. 40, 281–291 (1989)
    MATH Google Scholar
  17. Keeney, R.L., Raiffa, H.: Decisions with Multiple Objectives: Preferences and Value Tradeoffs. Wiley, New York (1976)
    Google Scholar
  18. Saaty, T.L.: The Analytic Hierarchy Process. McGraw-Hill, New York (1980)
    MATH Google Scholar
  19. Jacquet-Lagreze, E., Siskos, Y.: Invited Review: Preference disaggregation: 20 years of MCDA experience. Eur. J. Oper. Res. 130, 233–245 (2001)
    Article MATH Google Scholar

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Authors and Affiliations

  1. Department of Business Administration, Hansung University, 389 Samsun 3, Sungbuk, Seoul, 136-792, Korea
    Byeong Seok Ahn
  2. Department of Business Administration, Hanyang University, 1271 Sa-1, Sangrok, Ansan, Gyeonggi, 426-791, Korea
    Chang Hee Han

Authors

  1. Byeong Seok Ahn
  2. Chang Hee Han

Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Perugia, via Vanvitelli, 1, I-06123, Perugia, Italy
    Osvaldo Gervasi
  2. Department of Computer Science, University of Calgary, 2500 University Drive N.W., T2N 1N4, Calgary, AB, Canada
    Marina L. Gavrilova
  3. William Norris Professor, Head of the Computer Science and Engineering Department, University of Minnesota, USA
    Vipin Kumar
  4. Department of Chemistry, University of Perugia, Via Elce di Sotto, 8, I-06123, Perugia, Italy
    Antonio Laganá
  5. Institute of High Performance Computing, IHCP, 1 Science Park Road, 01-01 The Capricorn, Singapore Science Park II, 117528, Singapore
    Heow Pueh Lee
  6. School of Computing, Soongsil University, Seoul, Korea
    Youngsong Mun
  7. Clayton School of IT, Monash University, 3800, Clayton, Australia
    David Taniar
  8. OptimaNumerics Ltd, Belfast, United Kingdom
    Chih Jeng Kenneth Tan

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© 2005 Springer-Verlag Berlin Heidelberg

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Ahn, B.S., Han, C.H. (2005). Determining Multiple Attribute Weights Consistent with Pairwise Preference Orders. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2005. ICCSA 2005. Lecture Notes in Computer Science, vol 3483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424925\_39

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