Using data visualization for ranking alternatives with partial information and interactive tradeoff elicitation

  • Eduarda Asfora Frej
  • Adiel Teixeira de AlmeidaEmail author
  • Ana Paula Cabral Seixas Costa
Original paper


This paper puts forward the use of data visualization in a new method for solving multiple criteria decision-making problems for ranking of alternatives, based on Flexible and Interactive Tradeoff (FITradeoff) elicitation. This approach uses partial information about the decision maker’s preferences, based on a structured process for eliciting scale constants (or weights) within the scope of multi-attribute value theory. Different from most of the partial information methods present in the literature, our approach is based on the traditional Tradeoff, which is the most axiomatically founded procedure for elicitation of criteria weights. Pairwise dominance concept is incorporated into the mathematical model of FITradeoff in such way that, at each interaction with the decision maker, a partial—or complete—order of the alternatives can be achieved, based on a two-step algorithm proposed. The method is operated by means of a decision support system, which provides graphical visualization of the ranking at each interaction, in order to support the decision-making process with a simpler visualization of dominance relations. The proposed method is applied for solving a practical case of supplier selection in a food industry.


Ranking visualization Partial information Decision support system Multiple criteria decision-making FTTradeoff 



The authors would like to acknowledge CNPq for the partial financial support for this research.


This work was supported by the National Council for Scientific and Technological Development (CNPq).

Compliance with ethical standrds

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Ahn BS, Park KS (2008) Comparing methods for multiattribute decision making with ordinal weights. Comput Oper Res 35(5):1660–1670CrossRefGoogle Scholar
  2. Athanassopoulos AD, Podinovski VV (1997) Dominance and potential optimality in multiple criteria decision analysis with imprecise information. J Oper Res Soc 48(2):142–150CrossRefGoogle Scholar
  3. Barron FH, Barrett BE (1996) Decision quality using ranked attribute weights. Manage Sci 42(11):1515–1523CrossRefGoogle Scholar
  4. Belton V, Stewart T (2002) Multiple criteria decision analysis: an integrated approach. Springer, BerlinCrossRefGoogle Scholar
  5. Borcherding K, Eppel T, Von Winterfeldt D (1991) Comparison of weighting judgments in multiattribute utility measurement. Manage Sci 37(12):1603–1619CrossRefGoogle Scholar
  6. Ciomek K, Kadziński M, Tervonen T (2017) Heuristics for selecting pair-wise elicitation questions in multiple criteria choice problems. Eur J Oper Res 262(2):693–707CrossRefGoogle Scholar
  7. Danielson M, Ekenberg L, He Y (2014) Augmenting ordinal methods of attribute weight approximation. Decis Anal 11(1):21–26CrossRefGoogle Scholar
  8. De Almeida AT, Roselli LRP (2017) Visualization for decision support in FITradeoff method: exploring its evaluation with cognitive neuroscience. In: Linden I, Liu C, Colot C (eds) Decision Support Systems VII. Data, Information and Knowledge Visualization in Decision Support Systems. LNBIP, vol 282., pp 1–13. Google Scholar
  9. De Almeida AT, Almeida JA, Costa APCS, Almeida-Filho AT (2016) A new method for elicitation of criteria weights in additive models: flexible and interactive tradeoff. Eur J Oper Res 250(1):179–191CrossRefGoogle Scholar
  10. Dell’Ovo M, Frej EA, Oppio A, Capolongo S, Morais DC, De Almeida AT (2017) Multicriteria decision making for healthcare facilities location with visualization based on FITradeoff method. In: Linden I, Liu S, Colot C (eds) Decision Support Systems VII. Data, Information and Knowledge Visualization in Decision Support Systems. LNBIP, vol 282., pp 32–44. CrossRefGoogle Scholar
  11. Edwards W, Barron FH (1994) SMARTS and SMARTER: improved simple methods for multiattribute utility measurement. Organ Behav Hum Decis Process 60(3):306–325CrossRefGoogle Scholar
  12. Gusmão APH, Medeiros CP (2016) A model for selecting a strategic information system using the FITradeoff. Math Probl Eng. ID 7850960Google Scholar
  13. Keeney RL, Raiffa H (1976) Decision analysis with multiple conflicting objectives. Wiley, New YorkGoogle Scholar
  14. Kirkwood CW, Sarin RK (1985) Ranking with partial information: a method and an application. Oper Res 33(1):38–48CrossRefGoogle Scholar
  15. López JCL, Carrillo PAÁ, Chavira DAG, Noriega JJS (2017) A web-based group decision support system for multicriteria ranking problems. Oper Res Int J 17(2):499–534CrossRefGoogle Scholar
  16. Malakooti B (2000) 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(3):355–368CrossRefGoogle Scholar
  17. Mármol AM, Puerto J, Fernández FR (2002) Sequential incorporation of imprecise information in multiple criteria decision processes. Eur J Oper Res 137(1):123–133CrossRefGoogle Scholar
  18. Montiel LV, Bickel JE (2014) A generalized sampling approach for multilinear utility functions given partial preference information. Decis Anal 11(3):147–170CrossRefGoogle Scholar
  19. Mustajoki J, Hämäläinen RP, Salo A (2005) Decision support by interval SMART/SWING—incorporating imprecision in the SMART and SWING methods. Decis Sci 36(2):317–339CrossRefGoogle Scholar
  20. Park K (2004) 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(5):601–614CrossRefGoogle Scholar
  21. Park KS, Kim SH (1997) Tools for interactive multiattribute decision-making with incompletely identified information. Eur J Oper Res 98(1):111–123CrossRefGoogle Scholar
  22. Park KS, Lee KS, Eum YS, Park K (2001) Extended methods for identifying dominance and potential optimality in multi-criteria analysis with imprecise information. Eur J Oper Res 134(3):557–563CrossRefGoogle Scholar
  23. Salo AA, Hamalainen RP (2001) Preference ratios in multiattribute evaluation (PRIME)-elicitation and decision procedures under incomplete information. IEEE Trans Syst Man Cybern Part A: Syst Hum 31(6):533–545CrossRefGoogle Scholar
  24. Salo AA, Hämäläinen RP (1992) Preference assessment by imprecise ratio statements. Oper Res 40(6):1053–1061CrossRefGoogle Scholar
  25. Salo A, Punkka A (2005) Rank inclusion in criteria hierarchies. Eur J Oper Res 163(2):338–356CrossRefGoogle Scholar
  26. Sarabando P, Dias LC (2010) Simple procedures of choice in multicriteria problems without precise information about the alternatives’ values. Comput Oper Res 37(12):2239–2247CrossRefGoogle Scholar
  27. Stillwell WG, Seaver DA, Edwards W (1981) A comparison of weight approximation techniques in multiattribute utility decision making. Organ Behav Hum Perform 28(1):62–77CrossRefGoogle Scholar
  28. Weber M (1987) Decision making with incomplete information. Eur J Oper Res 28(1):44–57CrossRefGoogle Scholar
  29. Weber M, Borcherding K (1993) Behavioral influences on weight judgments in multiattribute decision making. Eur J Oper Res 67(1):1–12CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.CDSID - Center for Decision Systems and Information DevelopmentUniversidade Federal de PernambucoRecifeBrazil

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