Skip to main content
SpringerLink
Log in

Deriving the partial values in MCDM by goal programming

  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

Preference for a set of alternatives evaluated under multiple criteria is frequently expressed in the form of pairwise comparisons. We propose a linear goal programming model for deriving the partial and overall preference values of the alternatives directly from pairwise comparisons. This model can be a useful alternative to AHP. The partial values represent the contribution of the criteria to the overall preference. Simulation experiments, which are conducted to compare the performance of the model with that of two other existing models, show that the model has good performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. T.L. Saaty, Multicriteria decision making: The Analytic Hierarchy Process, RWS Publication, Pittsburgh, PA, 1990.

    Google Scholar 

  2. A. Arbel, Approximate articulation of preference and priority derivation, Euro. J. Oper. Res. 43 (1989)317–326.

    Article  Google Scholar 

  3. N. Bryson, A goal programming method for generating priority vectors, J. Oper. Res. Soc. 46 (1995)641–648.

    Article  Google Scholar 

  4. W.D. Cook and M. Kress, Deriving weights from pairwise comparison ratio matrices: An axiomatic approach, Euro. J. Oper. Res. 37(1988)355–362.

    Article  Google Scholar 

  5. J. Fichtner, On deriving priority vectors from matrices of pairwise comparisons, Socio-Econ. Plann. Sci. 20(1986)341–345.

    Article  Google Scholar 

  6. A. Hashimoto, A note on deriving weights from pairwise comparison ratio matrices, Euro. J. Oper. Res. 73(1994)144–149.

    Article  Google Scholar 

  7. P.E. Green and V. Srinivasan, Conjoint analysis in consumer research: Issues and outlook, J. Consumer Res. 5(1978)103–123.

    Article  Google Scholar 

  8. D. Pekelman and S.K. Sen, Mathematical programming models for the determination of attribute weights, Mgmt. Sci. 23(1974)1217–1229.

    Article  Google Scholar 

  9. V. Srinivasan, Linear programming computational procedures for ordinal regression, J. Assoc. Comp. Mach. 23(1975)475–487.

    Google Scholar 

  10. E.U. Choo and W.C. Wedley, Optimal criterion weights in repetitive multicriteria decision-making, J. Oper. Res. Soc. 36(1985)983–992.

    Article  Google Scholar 

  11. K.F. Lam and E.U. Choo, Goal programming in preference decomposition, J. Oper. Res. Soc. 46(1995)205–213.

    Article  Google Scholar 

  12. J. Johnston, Econometrics Methods, 2nd ed., McGraw-Hill, 1972.

  13. T.J. Stewart, A critical survey on the status of multiple criteria decision making theory and practice, Omega 20(1990)569–586.

    Article  Google Scholar 

  14. A.A. Salo and R.P. Hamalainen, Preference programming through approximate ratio comparisons, Euro. J. Oper. Res. 82(1995)458–475.

    Article  Google Scholar 

  15. P.E. Green and V. Srinivasan, Conjoint analysis in marketing: New developments with implications for research and practice, J. Mktg. 54(1990)3–19.

    Google Scholar 

  16. N. Bryson, A. Mobolurin and O. Ngwenyama, Modelling pairwise comparison on ratio scales, Euro. J. Oper. Res. 83(1995)639–654.

    Article  Google Scholar 

  17. B. Schoner, W.C. Wedley and E.U. Choo, A unified approach to AHP with linking pins, Euro. J. Oper. Res. 64(1993)384–392.

    Article  Google Scholar 

  18. B. Schoner and W.C. Wedley, Ambiguous criteria weights in AHP — consequences and solutions, Decision Sciences 20(1989)462–475.

    Google Scholar 

  19. T.L. Saaty, Eigenvector and logarithmic least squares, Euro. J. Oper. Res. 48(1990)156–160.

    Article  Google Scholar 

  20. D.R. Wittink and P. Cattin, Alternative estimation methods for conjoint analysis: A Monte Carlo study, J. Mktg. Res. 18(1981)101–106.

    Google Scholar 

  21. T.W. Leigh, D.B. Mackay and J.O. Summers, Reliability and validity of conjoint analysis and self-explicated weights: A comparison, J. Mktg. Res. 21(1984)456–462.

    Google Scholar 

  22. P.E. Green and K. Helsen, Cross-validation assessment of alternatives to individual-level conjoint analysis: A case study, J. Mktg. Res. 26(1989)346–350.

    Google Scholar 

  23. R. Mehta, W.L. Moore and T.M. Pavia, An examination of the use of unacceptable levels in conjoint analysis, J. Consumer Res. 19(1992)470–476.

    Article  Google Scholar 

  24. P.E. Green, A.M. Krieger and C.M. Schaffer, An empirical test of optimal respondent weighting in conjoint analysis, Journal of the Academy of Marketing Science 21(1993)345–351.

    Google Scholar 

  25. Conjoint Analyzer, Bretton-Clark, Morristown, NJ, 1989.

Download references

Authors
  1. Jane W. Moy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kim Fung Lam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Eng Ung Choo
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Moy, J.W., Fung Lam, K. & Ung Choo, E. Deriving the partial values in MCDM by goal programming. Annals of Operations Research 74, 277–288 (1997). https://doi.org/10.1023/A:1018930723267

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1018930723267

Search

Navigation