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Solving Multicriteria Group Decision-Making (MCGDM) Problems Based on Ranking with Partial Information

Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 351)

Abstract

This paper presents an interactive Decision Support System for solving multicriteria group decision-making (MCGDM) problems, based on partial information obtained from the decision makers (DMs). The decision support tool was built based on the concept of flexible elicitation of the FITradeoff method, with graphical visualization features and a user-friendly interface. The decision model is based on searching for dominance relations between alternatives, according to the preferential information obtained from the decision-makers from tradeoff questions. A partial (or complete) ranking of the alternatives is built based on these dominance relations, which are obtained from linear programming models. The system shows, at each interaction, an overview of the process, with the partial results for all decision-makers. The visualization of the individual rankings by all DMs can help them to achieve an agreement during the process, since they will be able to see how their preferred alternatives are in the ranking of the other DMs. The applicability of the system is illustrated here with a problem for selecting a package to improve safety of oil tankers.

Keywords

Multicriteria group decision-making (MCGDM) FITradeoff Partial information Ranking 

Notes

Acknowledgments

The authors are most grateful for CNPq and CAPES, for the financial support provided.

References

  1. 1.
    Ahn, B.S., Park, K.S.: Comparing methods for multiattribute decision making with ordinal weights. Comput. Oper. Res. 35, 1660–1670 (2008).  https://doi.org/10.1016/j.cor.2006.09.026CrossRefzbMATHGoogle Scholar
  2. 2.
    Athanassopoulos, A.D., Podinovski, V.V.: Dominance and potential optimality in multiple criteria decision analysis with imprecise information. J. Oper. Res. Soc. 48, 142–150 (1997).  https://doi.org/10.1057/palgrave.jors.2600345CrossRefzbMATHGoogle Scholar
  3. 3.
    Belton, V., Stewart, T.: Multiple Criteria Decision Analysis: An Integrated Approach. Springer, Heidelberg (2002).  https://doi.org/10.1007/978-1-4615-1495-4CrossRefGoogle Scholar
  4. 4.
    Danielson, M., Ekenberg, L.: A robustness study of state-of-the-art surrogate weights for MCDM. Group Decis. Negot. 26, 677–691 (2017).  https://doi.org/10.1007/s10726-016-9494-6CrossRefGoogle Scholar
  5. 5.
    Danielson, M., Ekenberg, L., Larsson, A., Riabacke, M.: Weighting under ambiguous preferences and imprecise differences in a cardinal rank ordering process. Int. J. Comput. Intell. Syst. 7, 105–112 (2014).  https://doi.org/10.1080/18756891.2014.853954CrossRefGoogle Scholar
  6. 6.
    de Almeida, A.T., Cavalcante, C.A.V., Alencar, M.H., Ferreira, R.J.P., Almeida-Filho, A.T., Garcez, T.V.: Multicriteria and multiobjective models for risk, reliability and maintenance decision analysis. International Series in Operations Research & Management Science, vol. 231. Springer, New York (2015).  https://doi.org/10.1007/978-3-319-17969-8CrossRefzbMATHGoogle Scholar
  7. 7.
    de Almeida, A.T., de Almeida, J.A., Costa, A.P.C.S., de Almeida-Filho, A.T.: A new method for elicitation of criteria weights in additive models: flexible and interactive tradeoff. Eur. J. Oper. Res. 250, 179–191 (2016).  https://doi.org/10.1016/j.ejor.2015.08.058MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    de Almeida, A.T., Wachowicz, T.: Preference analysis and decision support in negotiations and group decisions. Group Decis. Negot. 26, 649–652 (2017).  https://doi.org/10.1007/s10726-017-9538-6CrossRefGoogle Scholar
  9. 9.
    Dias, L.C., Clı́maco, J.N.: Dealing with imprecise information in group multicriteria decisions: a methodology and a GDSS architecture. Eur. J. Oper. Res. 160, 291–307 (2005).  https://doi.org/10.1016/j.ejor.2003.09.002CrossRefzbMATHGoogle Scholar
  10. 10.
    Dias, L.C., Clímaco, J.N.: Additive aggregation with variable interdependent parameters: the VIP analysis software. J. Oper. Res. Soc. 51, 1070–1082 (2000).  https://doi.org/10.1057/palgrave.jors.2601012CrossRefzbMATHGoogle Scholar
  11. 11.
    Edwards, W., Barron, F.H.: SMARTS and SMARTER: improved simple methods for multiattribute utility measurement. Organ. Behav. Hum. Decis. Process. 60, 306–325 (1994).  https://doi.org/10.1006/obhd.1994.1087CrossRefGoogle Scholar
  12. 12.
    Frej, E.A., de Almeida, A.T., Cabral, A.P.C.S.: Using data visualization for ranking alternatives with partial information and interactive tradeoff elicitation. Oper. Res. (2019).  https://doi.org/10.1007/s12351-018-00444-2
  13. 13.
    Keeney, R.L., Raiffa, H.: Decision Analysis with Multiple Conflicting Objectives. Wiley, New York (1976)Google Scholar
  14. 14.
    Kirkwood, C.W., Corner, J.L.: The effectiveness of partial information about attribute weights for ranking alternatives in multiattribute decision making. Organ. Behav. Hum. Decis. Process. 54, 456–476 (1993).  https://doi.org/10.1006/obhd.1993.1019CrossRefGoogle Scholar
  15. 15.
    Kirkwood, C.W., Sarin, R.K.: Ranking with partial information: a method and an application. Oper. Res. 33, 38–48 (1985).  https://doi.org/10.1287/opre.33.1.38MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Malakooti, B.: Ranking and screening multiple criteria alternatives with partial information and use of ordinal and cardinal strength of preferences. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum. 30, 355–368 (2000).  https://doi.org/10.1109/3468.844359CrossRefGoogle Scholar
  17. 17.
    Mármol, A.M., Puerto, J., Fernández, F.R.: Sequential incorporation of imprecise information in multiple criteria decision processes. Eur. J. Oper. Res. 137, 123–133 (2002).  https://doi.org/10.1016/s0377-2217(01)00082-0MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Montiel, L.V., Bickel, J.E.: A generalized sampling approach for multilinear utility functions given partial preference information. Decis. Anal. 11, 147–170 (2004).  https://doi.org/10.1287/deca.2014.0296MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Mustajoki, J., Hämäläinen, R.P., Salo, A.: Decision support by interval SMART/SWING - incorporating imprecision in the SMART and SWING methods. Decis. Sci. 36, 317–339 (2005).  https://doi.org/10.1111/j.1540-5414.2005.00075.xCrossRefGoogle Scholar
  20. 20.
    Park, K.S.: Mathematical programming models for characterizing dominance and potential optimality when multicriteria alternative values and weights are simultaneously incomplete. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum. 34, 601–614 (2004).  https://doi.org/10.1109/tsmca.2004.832828CrossRefGoogle Scholar
  21. 21.
    Park, K.S., Kim, S.H.: Tools for interactive multiattribute decision-making with incompletely identified information. Eur. J. Oper. Res. 98, 111–123 (1997).  https://doi.org/10.1016/0377-2217(95)00121-2CrossRefzbMATHGoogle Scholar
  22. 22.
    Salo, A.A., Hämäläinen, R.P.: Preference assessment by imprecise ratio statements. Oper. Res. 40, 1053–1061 (1992).  https://doi.org/10.1287/opre.40.6.1053CrossRefzbMATHGoogle Scholar
  23. 23.
    Salo, A.A., Hämälainen, R.P.: Preference ratios in multiattribute evaluation (PRIME)-elicitation and decision procedures under incomplete information. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum. 31, 533–545 (2001).  https://doi.org/10.1109/3468.983411CrossRefGoogle Scholar
  24. 24.
    Salo, A.A., Punkka, A.: Rank inclusion in criteria hierarchies. Eur. J. Oper. Res. 163, 338–356 (2005).  https://doi.org/10.1016/j.ejor.2003.10.014MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Sarabando, P., Dias, L.C.: Simple procedures of choice in multicriteria problems without precise information about the alternatives’ values. Comput. Oper. Res. 37, 2239–2247 (2010).  https://doi.org/10.1016/j.cor.2010.03.014MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Sarabando, P., Dias, L.C.: Multiattribute choice with ordinal information: a comparison of different decision rules. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum. 39, 545–554 (2009).  https://doi.org/10.1109/tsmca.2009.2014555CrossRefGoogle Scholar
  27. 27.
    Stillwell, W.G., Seaver, D.A., Edwards, W.: A comparison of weight approximation techniques in multiattribute utility decision making. Organ. Behav. Hum. Perform. 28, 62–77 (1981).  https://doi.org/10.1016/0030-5073(81)90015-5CrossRefGoogle Scholar
  28. 28.
    Ulvila, J.W., Snider, W.D.: Negotiation of international oil tanker standards: an application of multiattribute value theory. Oper. Res. 28, 81–96 (1980).  https://doi.org/10.1287/opre.28.1.81CrossRefGoogle Scholar
  29. 29.
    Weber, M.: Decision making with incomplete information. Eur. J. Oper. Res. 28, 44–57 (1987).  https://doi.org/10.1016/0377-2217(87)90168-8CrossRefzbMATHGoogle Scholar
  30. 30.
    Roselli, L.R.P., de Almeida, A.T., Frej, E.A.: Decision neuroscience for improving data visualization of decision support in the FITradeoff method. Oper. Res. (2019).  https://doi.org/10.1007/s12351-018-00445-1
  31. 31.
    Roselli, L.R.P., Frej, E.A., de Almeida, A.T.: Neuroscience experiment for graphical visualization in the FITradeoff Decision Support System. In: Chen, Y., Kersten, G., Vetschera, R., Xu, H. (eds.) GDN 2018. LNBIP, vol. 315, pp. 56–69. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-92874-6_5CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.CDSID - Center for Decision Systems and Information DevelopmentUniversidade Federal de Pernambuco – UFPERecifeBrazil

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