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New Rank-Reversal Free Approach to Handle Interval Data in MCDA Problems

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Computational Science – ICCS 2021 (ICCS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12747))

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Abstract

In many real-life decision-making problems, decisions have to be based on partially incomplete of uncertain data. Since classical MCDA methods were created to be used with numerical data, they are often unable to process incomplete or uncertain data. There are several ways to handle uncertainty and incompleteness in the data, i.e., interval numbers, fuzzy numbers, and their generalizations. New methods are developed, and classical methods are modified to work with incomplete and uncertain data. In this paper, we propose an extension of the SPOTIS method, which is a new rank-reversal free MCDA method. Our extension allows for applying this method to decision problems with missing or uncertain data. The proposed approach is compared in two study cases with other MCDA methods: COMET and TOPSIS. Obtained rankings would be analyzed using rank correlation coefficients.

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References

  1. Aires, R.F.F., Ferreira, L.: The rank reversal problem in multi-criteria decision making: a literature review. Pesqui. Oper. 38(2), 331–362 (2018)

    Article  Google Scholar 

  2. Behzadian, M., Otaghsara, S.K., Yazdani, M., Ignatius, J.: A state-of the-art survey of TOPSIS applications. Exp. Syst. Appl. 39(17), 13051–13069 (2012)

    Article  Google Scholar 

  3. Ceballos, B., Pelta, D.A., Lamata, M.T.: Rank reversal and the VIKOR method: an empirical evaluation. Int. J. Inf. Technol. Decis. Making 17(02), 513–525 (2018)

    Article  Google Scholar 

  4. Dezert, J., Tchamova, A., Han, D., Tacnet, J.M.: The SPOTIS rank reversal free method for multi-criteria decision-making support. In: 2020 IEEE 23rd International Conference on Information Fusion (FUSION), pp. 1–8. IEEE (2020)

    Google Scholar 

  5. Dubois, D., Prade, H.: Fuzzy numbers: an overview. In: Readings in Fuzzy Sets for Intelligent Systems, pp. 112–148. Elsevier (1993)

    Google Scholar 

  6. Faizi, S., Rashid, T., Sałabun, W., Zafar, S., Wątróbski, J.: Decision making with uncertainty using hesitant fuzzy sets. Int. J. Fuzzy Syst. 20(1), 93–103 (2018)

    Article  MathSciNet  Google Scholar 

  7. García-Cascales, M.S., Lamata, M.T.: On rank reversal and TOPSIS method. Math. Comput. Model. 56(5–6), 123–132 (2012)

    Article  MathSciNet  Google Scholar 

  8. Figueira, J.É., Greco, S., Ehrogott, M.: Multiple Criteria Decision Analysis: State of the Art Surveys. ISORMS, vol. 78. Springer, New York (2005). https://doi.org/10.1007/b100605

    Book  Google Scholar 

  9. Gu, Y., Zhang, S., Zhang, M.: Interval number comparison and decision making based on priority degree. In: Cao, B.-Y., Wang, P.-Z., Liu, Z.-L., Zhong, Y.-B. (eds.) International Conference on Oriental Thinking and Fuzzy Logic. AISC, vol. 443, pp. 197–205. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-30874-6_19

    Chapter  Google Scholar 

  10. Kizielewicz, B., Sałabun, W.: A new approach to identifying a multi-criteria decision model based on stochastic optimization techniques. Symmetry 12(9), 1551 (2020)

    Article  Google Scholar 

  11. Mareschal, B., De Smet, Y., Nemery, P.: Rank reversal in the PROMETHEE II method: some new results. In: 2008 IEEE International Conference on Industrial Engineering and Engineering Management, pp. 959–963. IEEE (2008)

    Google Scholar 

  12. Papathanasiou, J., Ploskas, N.: Multiple Criteria Decision Aid. SOIA, vol. 136. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91648-4

    Book  MATH  Google Scholar 

  13. Sałabun, W.: The mean error estimation of TOPSIS method using a fuzzy reference models. J. Theor. Appl. Comput. Sci. 7(3), 40–50 (2013)

    Google Scholar 

  14. Sałabun, W.: The characteristic objects method: a new distance-based approach to multicriteria decision-making problems. J. Multi-Criteria Decis. Anal. 22(1–2), 37–50 (2015)

    Article  Google Scholar 

  15. Sałabun, W., Karczmarczyk, A.: Using the COMET method in the sustainable city transport problem: an empirical study of the electric powered cars. Procedia Comput. Sci. 126, 2248–2260 (2018)

    Article  Google Scholar 

  16. Sałabun, W., Karczmarczyk, A., Wątróbski, J.: Decision-making using the hesitant fuzzy sets comet method: an empirical study of the electric city buses selection. In: 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1485–1492. IEEE (2018)

    Google Scholar 

  17. Sałabun, W., Karczmarczyk, A., Wątróbski, J., Jankowski, J.: Handling data uncertainty in decision making with COMET. In: 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1478–1484. IEEE (2018)

    Google Scholar 

  18. Sałabun, W., Palczewski, K., Wątróbski, J.: Multicriteria approach to sustainable transport evaluation under incomplete knowledge: electric bikes case study. Sustainability 11(12), 3314 (2019)

    Article  Google Scholar 

  19. Sałabun, W., Urbaniak, K.: A new coefficient of rankings similarity in decision-making problems. In: Krzhizhanovskaya, V.V., et al. (eds.) ICCS 2020. LNCS, vol. 12138, pp. 632–645. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50417-5_47

    Chapter  Google Scholar 

  20. Sałabun, W., Wątróbski, J., Shekhovtsov, A.: Are MCDA methods benchmarkable? A comparative study of TOPSIS, VIKOR, COPRAS, and PROMETHEE II methods. Symmetry 12(9), 1549 (2020)

    Article  Google Scholar 

  21. Sengupta, A., Pal, T.K.: On comparing interval numbers. Eur. J. Oper. Res. 127(1), 28–43 (2000)

    Article  MathSciNet  Google Scholar 

  22. Shekhovtsov, A., Kołodziejczyk, J., Sałabun, W.: Fuzzy model identification using monolithic and structured approaches in decision problems with partially incomplete data. Symmetry 12(9), 1541 (2020)

    Article  Google Scholar 

  23. Torra, V.: Hesitant fuzzy sets. Int. J. Intell. Syst. 25(6), 529–539 (2010)

    MATH  Google Scholar 

  24. Utkin, L.V., Augustin, T.: Decision making under incomplete data using the imprecise Dirichlet model. Int. J. Approx. Reason. 44(3), 322–338 (2007)

    Article  MathSciNet  Google Scholar 

  25. Wang, Y.M., Luo, Y.: On rank reversal in decision analysis. Math. Comput. Model. 49(5–6), 1221–1229 (2009)

    Article  MathSciNet  Google Scholar 

  26. Wątróbski, J., Małecki, K., Kijewska, K., Iwan, S., Karczmarczyk, A., Thompson, R.G.: Multi-criteria analysis of electric vans for city logistics. Sustainability 9(8), 1453 (2017)

    Article  Google Scholar 

  27. Žižović, M., Pamučar, D., Albijanić, M., Chatterjee, P., Pribićević, I.: Eliminating rank reversal problem using a new multi-attribute model-the RAFSI method. Mathematics 8(6), 1015 (2020)

    Article  Google Scholar 

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Acknowledgments

The work was supported by the National Science Centre, Decision number UMO-2016/23/N/HS4/01931.

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, Department of Artificial Intelligence and Applied Mathematics, Research Team on Intelligent Decision Support Systems, West Pomeranian University of Technology in Szczecin, ul. Żołnierska 49, 71-210, Szczecin, Poland

    Andrii Shekhovtsov, Bartłomiej Kizielewicz & Wojciech Sałabun

Authors
  1. Andrii Shekhovtsov
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  2. Bartłomiej Kizielewicz
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  3. Wojciech Sałabun
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Corresponding author

Correspondence to Wojciech Sałabun .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Dieter Kranzlmüller

  3. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  4. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  5. University of Amsterdam, Amsterdam, The Netherlands

    Peter M. A. Sloot

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Shekhovtsov, A., Kizielewicz, B., Sałabun, W. (2021). New Rank-Reversal Free Approach to Handle Interval Data in MCDA Problems. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12747. Springer, Cham. https://doi.org/10.1007/978-3-030-77980-1_35

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  • DOI: https://doi.org/10.1007/978-3-030-77980-1_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77979-5

  • Online ISBN: 978-3-030-77980-1

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