Skip to main content
SpringerLink
Log in

A flexible elicitation procedure for additive model scale constants

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

Abstract

This paper contributes to the process of eliciting additive model scale constants in order to support choice problems, thereby reducing the effort a decision maker (DM) needs to make since partial information with regard to DM preferences can be used. Procedures related to eliciting weights without a tradeoff interpretation of weights are justified based on assumptions that DM is not able to specify fixed weight values or if DM is able to do so, this would not be reliable information. As long as partial information is provided, the flexible elicitation procedure performs dominance tests based on a linear programming problem to explore the DM’s preferences as a vector space which is built using the DM’s partial information. To provide evidence of the satisfactory performance of the flexible elicitation procedure, an empirical test is presented with results that indicate that this procedure requires less effort from DMs.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Barron, F. H. (1992). Selecting a best multiattribute alternative with partial information about attribute weights. Acta Psychologica, 80, 91–103.

    Article  Google Scholar 

  • Barron, F. H., & Barrett, B. E. (1996). Decision quality using ranked attribute weights. Science, 42, 1515–1523.

    Google Scholar 

  • Ben Amor, S., Zaras, K., & Aguayo, E. A. (2016). The value of additional information in multicriteria decision making choice problems with information imperfections. Annals of Operations Research. doi:10.1007/s10479-016-2318-x.

  • Borcheding, K., Eppel, T., & Winterfeldt, D. V. (1991). Comparison weight judgment in multiattribute utility measurement. Management Science, 37(8), 1603–1619.

    Article  Google Scholar 

  • Bottomley, P. A., & Doyle, J. R. (2001). A comparison of three weight elicitation methods: Good, better, and best. Omega, 29, 553–560.

    Article  Google Scholar 

  • Bottomley, P. A., Doyle, J. R., & Green, R. H. (2000). Testing the reliability of weight elicitation methods: Direct rating versus point allocation. Journal of Marketing Research, 37(4), 508–513.

    Article  Google Scholar 

  • Campos, A. C. S. M., Mareschal, B., & de Almeida, A. T. (2015). Fuzzy FlowSort: An integration of the FlowSort method and fuzzy set theory for decision making on the basis of inaccurate quantitative data. Information Sciences, 293, 115–124.

    Article  Google Scholar 

  • Chen, T. Y. (2014a). Interval-valued intuitionistic fuzzy QUALIFLEX method with a likelihood-based comparison approach for multiple criteria decision analysis. Information Sciences, 261, 149–169.

    Article  Google Scholar 

  • Chen, T. Y. (2014b). A prioritized aggregation operator-based approach to multiple criteria decision making using interval-valued intuitionistic fuzzy sets: A comparative perspective. Information Sciences, 28, 97–112.

    Article  Google Scholar 

  • Dabbene, F., Shcherbakov, P. S., & Polyak, B. T. (2010). A randomized cutting plane method with probabilistic geometric convergence. SIAM Journal on Optimization, 20, 3185–3207.

    Article  Google Scholar 

  • Danielson, M., & Ekenberg, L. (2007). Computing upper and lower bounds in interval decision trees. European Journal of Operational Research, 181(2), 808–816.

    Article  Google Scholar 

  • de Almeida, A. T. (2006). Multicriteria decision model for outsourcing contracts selection based on utility function and ELECTRE method. Computers & Operations Research, 34, 3569–3574.

    Article  Google Scholar 

  • de Almeida, A. T., de Almeida, J. A., Costa, A. P. C. S., & de Almeida-Filho, A. T. (2016). A new method for elicitation of criteria weights in additive models: Flexible and interactive tradeoff. European Journal of Operational Research, 250, 179–191.

    Article  Google Scholar 

  • Edwards, W. (1977). How to use multiattribute utility measurement for social decisionmaking. IEEE Transactions on Systems, Man and Cybernetics, 7(5), 326–340.

    Article  Google Scholar 

  • Edwards, W., & Barron, F. H. (1994). SMARTS and SMARTER: Improved simple methods for multiattribute utility measurement. Organizational Behavior and Human Decision Processes, 60(3), 306–325.

    Article  Google Scholar 

  • Eum, Y. S., Park, K. S., & Kim, S. H. (2001). Establishing dominance and potential optimality in multi-criteria analysis with imprecise weight and value. Computers & Operations Research, 28, 397–409.

    Article  Google Scholar 

  • Franc, V., & Sonnenburg, S. (2009). Optimized cutting plane algorithm for large-scale risk minimization. Journal of Machine Learning Research, 10, 2157–2192.

    Google Scholar 

  • Gusmão, A. P. H., & Medeiros, C. P. (2016). A model for selecting a strategic information system using the FITradeoff. Mathematical Problems in Engineering. doi:10.1155/2016/7850960.

  • Hazen, G. B. (1986). Partial information, dominance, and potential optimality in multiattribute utility theory. Operations Research, 34(2), 296–310.

    Article  Google Scholar 

  • Jia, J., Fischer, G. W., & Dyer, J. S. (1998). Attribute weighting methods and decision quality in the presence of response error: A simulation study. Journal of Behavioral Decision Making, 11, 85–105.

    Article  Google Scholar 

  • Jimenez, A., Rıos-Insua, S., & Mateos, A. (2003). A decision support system for multiattribute utility evaluation based on imprecise assignments. Decision Support Systems, 36, 65–79.

    Article  Google Scholar 

  • Keeney, R. L., & Raiffa, H. (1976). Decision making with multiple objectives, preferences, and value tradeoffs. New York: Wiley.

    Google Scholar 

  • Kirkwood, C. W., & Sarin, R. K. (1985). Ranking with partial information: A method and an application. Operations Research, 33, 38–48.

    Article  Google Scholar 

  • Larichev, O. I. (1992). Cognitive validity in design of decision-aiding techniques. Journal of Multicriteria Decision Analysis, 1(3), 127–138.

    Article  Google Scholar 

  • Li, J., Chen, Y., Yue, C., & Song, H. (2012). Dominance measuring-based approach for multi-attribute decision making with imprecise weights. Journal of Information & Computational Science, 9(8), 3305–3313.

    Google Scholar 

  • Lotfi, V. T., Stewart, T. J., & Zionts, S. (1992). An aspiration-level interactive model for multiple criteria decision making. Computers Operations Research, 19(7), 671–681.

    Article  Google Scholar 

  • Mármol, A. M., Puerto, J., & Fernández, F. R. (2002). Sequencial incorporation of imprecise information of multiple criteria decision processes. European Journal of Operational Research, 137, 123–133.

    Article  Google Scholar 

  • Medeiros, C. P., Alencar, M. H., & de Almeida, A. T. (2016). Hydrogen pipelines: Enhancing information visualization and statistical tests for global sensitivity analysis when evaluating multidimensional risks to support decision-making. International Journal of Hydrogen Energy, 41(47), 22192–22205. doi:10.1016/j.ijhydene.2016.09.113.

    Article  Google Scholar 

  • Mustajoki, J. (2005). Decision support by interval SMART/SWING–Incorporating Imprecision in the SMART and SWING methods. Decision Sciences, 36(2), 317–339.

    Article  Google Scholar 

  • Mustajoki, J. (2012). Effects of imprecise weighting in hierarchical preference programming. European Journal of Operational Research, 218, 193–201.

    Article  Google Scholar 

  • Palha, R. P., de Almeida, A. T., & Alencar, L. H. (2016). A model for sorting activities to be outsourced in civil construction based on ROR-UTADIS. Mathematical Problems in Engineering. doi:10.1155/2016/9236414.

  • Park, K. S. (2004). Mathematical programming models for characterizing dominance and potential optimality when multicriteria alternative values and weights are simultaneously incomplete. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 34(5), 601–614.

    Article  Google Scholar 

  • Parreiras, R. O., Ekel, P., & Bernardes, F. (2012). A dynamic consensus scheme based on a nonreciprocal fuzzy preference relation modeling. Information Sciences, 211, 1–17.

    Article  Google Scholar 

  • Parreiras, R. O., Ekel, P., Martini, J. S. C., & Palhares, R. M. (2010). A flexible consensus scheme for multicriteria group decision making under linguistic assessments. Information Sciences, 180, 1075–1089.

    Article  Google Scholar 

  • Pergher, I., & de Almeida, A. T. (2017). A multi-attribute decision model for setting production planning parameters. Journal of Manufacturing Systems, 42, 224–232. doi:10.1016/j.jmsy.2016.12.012.

    Article  Google Scholar 

  • Pöyhönen, M., & Hämäläinen, R. P. (2001). On the convergence of multiattribute weighting methods. European Journal of Operational Research, 129, 569–585.

    Article  Google Scholar 

  • Puerto, J., Mármolm, L. M., & Fernandez, F. R. (2000). Decision criteria with partial information. International Transactions in Operational Research, 7, 51–65.

    Article  Google Scholar 

  • Punkka, A., & Salo, A. (2013). Preference programming with incomplete ordinal information. European Journal of Operational Research, 231(1), 141–150. doi:10.1016/j.ejor.2013.05.003.

  • Riabacke, M., Danielson, M., & Ekenberg, L. (2012). State-of-the-art prescriptive criteria weight elicitation (p. 24). Cairo: Hindawi Publishing Corporation Advances in Decision Sciences.

    Google Scholar 

  • Saaty, T. L. (1980). The analytic hierarchy process. New York: McGraw-Hill.

    Google Scholar 

  • Salo, A., & Hämäläinen, R. P. (1992). Preference assessment by imprecise ratio statements. Operations Research, 40, 1053–1061.

    Article  Google Scholar 

  • Salo, A., & Hämäläinen, R. P. (2001). Preference ratios in multiattribute evaluation (PRIME)–elicitation and decision procedures under incomplete information. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 31, 533–545.

    Article  Google Scholar 

  • Salo, A., & Punkka, A. (2005). Rank inclusion in criteria hierarchies. European Journal of Operational Research, 163, 338–356.

    Article  Google Scholar 

  • Sarabando, P., & Dias, L. C. (2010). Simple procedures of choice in multicriteria problems without precise information about the alternatives’ values. Computers & Operations Research, 37, 2239–2247.

    Article  Google Scholar 

  • Siegel, S., & Castellan, N. J. (1988). Nonparametric statistics (2nd ed.). New York: McGraw-Hill.

    Google Scholar 

  • Steuer, R. E. (1976). Multiple objective linear programming, with interval criterion weights. Management Science, 23, 305–316.

    Article  Google Scholar 

  • Walley, P. (1991). Statistical reasoning with imprecise probabilities. London: Chapman and Hall.

    Book  Google Scholar 

  • Weber, M., & Borcherding, K. (1993). Behavioral influences on weight judgments in multiattribute decision making. European Journal of Operational Research, 67, 1–12.

    Article  Google Scholar 

  • Winterfeldt, D. V., & Edwards, W. (1986). Decision analysis and behavioral research. Cambridge: Cambridge University Press.

    Google Scholar 

Download references

Acknowledgements

This work had partial support of CNPq (Brazilian research council).

Author information

Authors and Affiliations

  1. Universidade Federal de Pernambuco, CDSID - Center for Decision Systems and Information Development, Av. Acadêmico Hélio Ramos, s/n – Cidade Universitária, Recife, PE, CEP, 50.740-530, Brazil

    Adiel T. de Almeida-Filho, Adiel T. de Almeida & Ana Paula C. S. Costa

Authors
  1. Adiel T. de Almeida-Filho
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Adiel T. de Almeida
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ana Paula C. S. Costa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Adiel T. de Almeida-Filho.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

de Almeida-Filho, A.T., de Almeida, A.T. & Costa, A.P.C.S. A flexible elicitation procedure for additive model scale constants. Ann Oper Res 259, 65–83 (2017). https://doi.org/10.1007/s10479-017-2519-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-017-2519-y

Keywords

Search

Navigation