PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Using Inconsistency Reduction Algorithms in Comparison Matrices to Improve the Performance of Generating Random Comparison Matrices with a Given Inconsistency Coefficient Range

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The aim of this paper is to present a new method for generating random pairwise comparison matrices with a given inconsistency ratio (CR) interval using inconsistency reduction algorithms. Pairwise comparison (PC) is a popular technique for multi-criteria decision-making, its purpose is to assign weights to the compared entities, thus ranking them from best to worst. The presented method combines the traditional random generation of comparison matrices supported by inconsistency reduction algorithms: the “Xu and Wei” algorithm and the “Szybowski” algorithm. This paper presents research that shows an increase in performance when generating such matrices relative to the standard random comparison matrix generation procedure using the “Szybowski” algorithm. The other algorithms also improve the process, but to a lesser extent, making the “Szybowski” supporting algorithm the preferred solution for the new process. As a result of the research, a free online tool “PC MATRICES GENERATOR” has also been made available to efficiently generate a large number of comparison matrices with a given CR factor range, any matrix size, and any number of matrices, enabling much more efficient and less time-consuming research in many fields that use comparison matrices, as the analytic hierarchy/network process (AHP/ANP), ELECTREE, PAPRIKA, PROMETHE, VIKOR or the Best-Worst method (BWM).
Twórcy
  • Department of Complex Systems, The Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, ul. MC Skłodowskiej 8, 35-036 Rzeszów, Poland
autor
  • Department of Complex Systems, The Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, ul. MC Skłodowskiej 8, 35-036 Rzeszów, Poland
Bibliografia
  • 1. Mazurek J. A New Approach to a Derivation of a Priority Vector from an Interval Comparison Matrix in a Group AHP Framework. In: International Conference on Intelligent Decision Technologies. Springer, Cham, 2016: 193-201.
  • 2. Liang F., Brunelli M., Rezaei J. Consistency issues in the best worst method: Measurements and thresholds. Omega 2020; 96: 102175.
  • 3. Saaty T.L. A scaling method for priorities in hierarchical structures. Journal of mathematical psychology 1977; 15(3): 234–281.
  • 4. Saaty T.L. The analytic hierarchy process McGraw-Hill. New York 1980; 324.
  • 5. Caflisch R.E. Monte carlo and quasi-monte carlo methods. Acta numerica 1998; 7: 1–49.
  • 6. Zeschui X., Cuiping W. A consistency improving method in the analytic hierarchy process. European journal of operational research 1999; 116(2): 443–449.
  • 7. Cao D., Leung L. C., Law J. S. Modifying inconsistent comparison matrix in analytic hierarchy process: A heuristic approach. Decision Support Systems 2008; 44(4): 944–953.
  • 8. Ergu D., Kou G., Peng Y., Shi Y. A simple method to improve the consistency ratio of the pair-wise comparison matrix in ANP. European Journal of Operational Research 2021; 213(1): 246–259.
  • 9. Benítez J., Delgado-Galván X., Gutiérrez J.A., Izquierdo J. Balancing consistency and expert judgment in AHP. Mathematical and Computer Modelling 2011; 54.7–8: 1785–1790.
  • 10. Benítez J., Delgado-Galván X., Izquierdo J., Pérez-García R. Improving consistency in AHP decision-making processes. Applied Mathematics and Computation 2012; 219.5: 2432–2441.
  • 11. Kułakowski K., Juszczyk R., Ernst S. A concurrent inconsistency reduction algorithm for the pairwise comparisons method. In: International Conference on Artificial Intelligence and Soft Computing. Springer, Cham 2015: 214–222.
  • 12. Szybowski J. The improvement of data in pairwise comparison matrices. Procedia Computer Science 2018; 126: 1006–1013.
  • 13. Abel E., Mikhailov L., Keane J. Inconsistency reduction in decision making via multi-objective optimisation. European Journal of Operational Research 2018; 267.1: 212–226.
  • 14. Mazurek J., Perzina R., Strzałka D., Kowal B. A new step-by-step (SBS) algorithm for inconsistency reduction in pairwise comparisons. IEEE Access 2020; 8: 135821–135828.
  • 15. Mazurek J., Perzina R., Strzałka D., Kowal B., Kuraś P. A numerical comparison of iterative algorithms for inconsistency reduction in pairwise comparisons. IEEE Access 2021; 9: 62553–62561.
  • 16. https://reduce.prz.edu.pl/pc_matrices_generator/.
  • 17. Brans J., Vincke P., Mareschal B. How to select and how to rank projects: The PROMETHEE method. European Journal of Operational Research, 1986; 24.2: 228–238.
  • 18. Brans, J.P., De Smet, Y. PROMETHEE methods. In Multiple criteria decision analysis. Springer, New York, NY 2016; 187–219.
  • 19. Smarzewski R., Kozera R. Constructive Consistent Approximations in Pairwise Comparisons. Advances in Science and Technology Research Journal 2022; 16.4: 243–255.
  • 20. Koczkodaj W.W., Orłowski M. An orthogonal basis for computing a consistent approximation to a pairwise comparisons matrix. Computers and Mathematics with Applications 1997; 34: 41–47.
  • 21. Koczkodaj W.W., Szarek S.J. On distance-based consistency reduction algorithms for pairwise comparisons. Logic Journal of the IGPL 2010; 18: 859–869.
  • 22. Koczkodaj W.W., Szwarc R. On axiomatization of inconsistency indicators for pairwise comparisons. Fundamenta Informaticae 2014; 132: 485–500.
  • 23. Koczkodaj W.W., Smarzewski R., Szybowski J. On orthogonal projections on the space of consistent pairwise comparisons matrices. Fundamenta Informaticae 2020; 172: 379–397.
  • 24. Cavallo B., Brunelli M. A general unified framework for interval pairwise comparisons matrices. International Journal of Approximate Reasoning 2018; 178–198: 93.
  • 25. Hansen P., Ombler F. A new method for scoring additive multi-attribute value models using pairwise rankings of alternatives. Journal of Multi-Criteria Decision Analysis 2008; 15.3-4: 87–107.
  • 26. Opricovic S., Tzeng G.H. Extended VIKOR method in comparison with outranking methods. European journal of operational research 2007; 178.2: 514–529.
  • 27. Alkhairi P., Purba L.P., Eryzha A., Windarto A.P., Wanto A. The analysis of the ELECTREE II algorithm in determining the doubts of the community doing business online. Journal of Physics: Conference Series. IOP Publishing 2019; 1255(1).
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6cf4342e-923c-4995-8ef5-a198c430b574
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.