Logo PTI Logo FedCSIS

Proceedings of the 16th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 25

Effect of Criteria Range on the Similarity of Results in the COMET Method

, , ,

DOI: http://dx.doi.org/10.15439/2021F44

Citation: Proceedings of the 16th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 25, pages 453457 ()

Full text

Abstract. In this paper, the Characteristic Objects Method (COMET) was used to investigate the overestimation effect on the final rankings. The decision matrixes with a different number of alternatives and criteria were assessed The obtained results were compared using the WS similarity coefficient and Spearman's weighted correlation coefficient. The study showed that overestimation has a significant effect on the rankings. A larger number of criteria has a positive effect on the correlation strength of the compared rankings. In contrast, a large overestimation of characteristic values has a negative effect on the similarity of the results.

References

  1. J. J. Jassbi, R. A. Ribeiro, and L. R. Varela, “Dynamic mcdm with future knowledge for supplier selection,” Journal of Decision Systems, vol. 23, no. 3, pp. 232–248, 2014.
  2. I. Vinogradova, V. Podvezko, and E. K. Zavadskas, “The recalculation of the weights of criteria in mcdm methods using the bayes approach,” Symmetry, vol. 10, no. 6, p. 205, 2018.
  3. D. S. Pamučar, D. Božanić, and A. Rand̄elović, “Multi-criteria decision making: An example of sensitivity analysis,” Serbian journal of management, vol. 12, no. 1, pp. 1–27, 2017.
  4. S. Opricovic and G.-H. Tzeng, “Compromise solution by mcdm methods: A comparative analysis of vikor and topsis,” European journal of operational research, vol. 156, no. 2, pp. 445–455, 2004.
  5. L. Ustinovichius, E. Zavadkas, and V. Podvezko, “Application of a quantitative multiple criteria decision making (mcdm-1) approach to the analysis of investments in construction,” Control and cybernetics, vol. 36, no. 1, p. 251, 2007.
  6. B. Kizielewicz, J. Więckowski, A. Shekhovtsov, E. Ziemba, J. Wątróbski, and W. Sałabun, “Input data preprocessing for the mcdm model: Copras method case study,” 2021.
  7. S. Opricovic and G.-H. Tzeng, “Extended vikor method in comparison with outranking methods,” European journal of operational research, vol. 178, no. 2, pp. 514–529, 2007.
  8. W. Sałabun, P. Ziemba, and J. Wątróbski, “The rank reversals paradox in management decisions: The comparison of the ahp and comet methods,” in International Conference on Intelligent Decision Technologies. Springer, 2016, pp. 181–191.
  9. A. Krylovas, E. K. Zavadskas, N. Kosareva, and S. Dadelo, “New kemira method for determining criteria priority and weights in solving mcdm problem,” International Journal of Information Technology & Decision Making, vol. 13, no. 06, pp. 1119–1133, 2014.
  10. D. Pamučar, Ž. Stević, and S. Sremac, “A new model for determining weight coefficients of criteria in mcdm models: Full consistency method (fucom),” Symmetry, vol. 10, no. 9, p. 393, 2018.
  11. W. Sałabun, A. Karczmarczyk, J. Wątróbski, and J. Jankowski, “Handling data uncertainty in decision making with comet,” in 2018 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2018, pp. 1478–1484.
  12. B. Kizielewicz and L. Dobryakova, “Mcda based approach to sports players’ evaluation under incomplete knowledge,” Procedia Computer Science, vol. 176, pp. 3524–3535, 2020.
  13. Y.-C. Chen, H.-P. Lien, and G.-H. Tzeng, “Measures and evaluation for environment watershed plans using a novel hybrid mcdm model,” Expert systems with applications, vol. 37, no. 2, pp. 926–938, 2010.
  14. Y.-C. Lee, P.-H. Chung, and J. Z. Shyu, “Performance evaluation of medical device manufacturers using a hybrid fuzzy mcdm,” 2017.
  15. O. Parkash and R. Kumar, “Modified fuzzy divergence measure and its applications to medical diagnosis and mcdm,” Risk and Decision Analysis, vol. 6, no. 3, pp. 231–237, 2017.
  16. Ž. Stević, D. Pamučar, A. Puška, and P. Chatterjee, “Sustainable supplier selection in healthcare industries using a new mcdm method: Measurement of alternatives and ranking according to compromise solution (marcos),” Computers & Industrial Engineering, vol. 140, p. 106231, 2020.
  17. J. Roy, K. Adhikary, S. Kar, and D. Pamucar, “A rough strength relational dematel model for analysing the key success factors of hospital service quality,” Decision Making: Applications in Management and Engineering, vol. 1, no. 1, pp. 121–142, 2018.
  18. V. Y. Chen, H.-P. Lien, C.-H. Liu, J. J. Liou, G.-H. Tzeng, and L.-S. Yang, “Fuzzy MCDM approach for selecting the best environment- watershed plan,” Applied soft computing, vol. 11, no. 1, pp. 265–275, 2011.
  19. A. Shekhovtsov, V. Kozlov, V. Nosov, and W. Sałabun, “Efficiency of methods for determining the relevance of criteria in sustainable transport problems: A comparative case study,” Sustainability, vol. 12, no. 19, p. 7915, 2020.
  20. Ž. Stević, D. Pamučar, M. Vasiljević, G. Stojić, and S. Korica, “Novel integrated multi-criteria model for supplier selection: Case study construction company,” Symmetry, vol. 9, no. 11, p. 279, 2017.
  21. M.-T. Lu, C.-C. Hsu, J. J. Liou, and H.-W. Lo, “A hybrid mcdm and sustainability-balanced scorecard model to establish sustainable performance evaluation for international airports,” Journal of Air Transport Management, vol. 71, pp. 9–19, 2018.
  22. M. Nassereddine and H. Eskandari, “An integrated mcdm approach to evaluate public transportation systems in tehran,” Transportation Research Part A: Policy and Practice, vol. 106, pp. 427–439, 2017.
  23. B. Kizielewicz, J. Więckowski, A. Shekhovtsov, J. Wątróbski, R. Depczyński, and W. Sałabun, “Study towards the time-based mcda ranking analysis–a supplier selection case study,” Facta Universitatis, Series: Mechanical Engineering, 2021.
  24. S. Faizi, W. Sałabun, S. Ullah, T. Rashid, and J. Więckowski, “A new method to support decision-making in an uncertain environment based on normalized interval-valued triangular fuzzy numbers and comet technique,” Symmetry, vol. 12, no. 4, p. 516, 2020.
  25. W. Sałabun, “Reduction in the number of comparisons required to create matrix of expert judgment in the comet method,” Management and Production Engineering Review, vol. 5, 2014.
  26. B. Kizielewicz and J. Kołodziejczyk, “Effects of the selection of characteristic values on the accuracy of results in the comet method,” Procedia Computer Science, vol. 176, pp. 3581–3590, 2020.
  27. W. Sałabun, J. Wątróbski, and A. Shekhovtsov, “Are MCDA methods benchmarkable? a comparative study of TOPSIS, VIKOR, COPRAS, and PROMETHEE II methods,” Symmetry, vol. 12, no. 9, p. 1549, 2020.
  28. W. Sałabun and K. Urbaniak, “A new coefficient of rankings similarity in decision-making problems,” in International Conference on Computational Science. Springer, 2020, pp. 632–645.
  29. W. Sałabun, A. Piegat, J. Wątróbski, A. Karczmarczyk, and J. Jankowski, “The comet method: The first mcda method completely resistant to rank reversal paradox,” European Working Group Series, vol. 3.
  30. K. Palczewski and W. Sałabun, “Identification of the football teams assessment model using the comet method,” Procedia Computer Science, vol. 159, pp. 2491–2501, 2019.
  31. W. Sałabun, A. Shekhovtsov, and B. Kizielewicz, “A new consistency coefficient in the multi-criteria decision analysis domain,” in International Conference on Computational Science. Springer, 2021, pp. 715–727.
  32. B. Kizielewicz and Z. Szyjewski, “Handling economic perspective in multicriteria model-renewable energy resources case study,” Procedia Computer Science, vol. 176, pp. 3555–3562, 2020.
  33. W. Sałabun, A. Shekhovtsov, D. Pamučar, J. Wątróbski, B. Kizielewicz, J. Więckowski, D. Bozanić, K. Urbaniak, and B. Nyczaj, “A fuzzy inference system for players evaluation in multi-player sports: The football study case,” Symmetry, vol. 12, no. 12, p. 2029, 2020.
  34. B. Kizielewicz and W. Sałabun, “A new approach to identifying a multicriteria decision model based on stochastic optimization techniques,” Symmetry, vol. 12, no. 9, p. 1551, 2020.
  35. J. Więckowski, B. Kizielewicz, and J. Kołodziejczyk, “Finding an approximate global optimum of characteristic objects preferences by using simulated annealing,” in International Conference on Intelligent Decision Technologies. Springer, 2020, pp. 365–375.
  36. ——, “The search of the optimal preference values of the characteristic objects by using particle swarm optimization in the uncertain environment,” in International Conference on Intelligent Decision Technologies. Springer, 2020, pp. 353–363.
  37. B. Kizielewicz, A. Shekhovtsov, and W. Sałabun, “A new approach to eliminate rank reversal in the mcda problems,” in International Conference on Computational Science. Springer, 2021, pp. 338–351.
  38. B. Kizielewicz and L. Dobryakova, “How to choose the optimal single-track vehicle to move in the city? electric scooters study case,” Procedia Computer Science, vol. 176, pp. 2243–2253, 2020.
  39. W. Sałabun, “The characteristic objects method: A new distance-based approach to multicriteria decision-making problems,” Journal of Multi-Criteria Decision Analysis, vol. 22, no. 1-2, pp. 37–50, 2015.
  40. A. Piegat and W. Sałabun, “Identification of a multicriteria decision-making model using the characteristic objects method,” Applied Computational Intelligence and Soft Computing, vol. 2014, 2014.
  41. W. Sałabun and A. Piegat, “Comparative analysis of MCDM methods for the assessment of mortality in patients with acute coronary syndrome,” Artificial Intelligence Review, vol. 48, no. 4, pp. 557–571, 2017.