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A Generic Multi-Level Algorithm for Prioritized Multi-Criteria Decision Making

  • G., AlShorbagy (Climate Change Information Center & Renewable Energy & Expert Systems) ;
  • Eslam, Hamouda (Faculty of Computers and Information Sciences, Mansoura University) ;
  • A.S., Abohamama (Faculty of Computers and Information Sciences, Mansoura University)
  • Received : 2023.01.05
  • Published : 2023.01.30

Abstract

Decision-making refers to identifying the best alternative among a set of alternatives. When a set of criteria are involved, the decision-making is called multi-criteria decision-making (MCDM). In some cases, the involved criteria may be prioritized by the human decision-maker, which determines the importance degree for each criterion; hence, the decision-making becomes prioritized multi-criteria decision-making. The essence of prioritized MCDM is raking the different alternatives concerning the criteria and selecting best one(s) from the ranked list. This paper introduces a generic multi-level algorithm for ranking multiple alternatives in prioritized MCDM problems. The proposed algorithm is implemented by a decision support system for selecting the most critical short-road requests presented to the transportation ministry in the Kingdom of Saudi Arabia. The ranking results show that the proposed ranking algorithm achieves a good balance between the importance degrees determined by the human decision maker and the score value of the alternatives concerning the different criteria.

Keywords

References

  1. Kahraman, C., Cebi, S.: A new multi-attribute decision-making method: Hierarchical fuzzy axiomatic design. Expert Systems with Applications, vol. 36(3), pp. 4848-4861 (2009). https://doi.org/10.1016/j.eswa.2008.05.041
  2. Deng, J., Zhan, J., Wu, W. Z.: A three-way decision methodology to multi-attribute decision-making in multiscale decision information systems. Information Sciences, vol. 568, pp. 175-198 (2021).
  3. Janis, I. L., Mann, L.:Decision making: A psychological analysis of conflict, choice, and commitment. Free press (1997).
  4. Plous, S.: The psychology of judgment and decision making. Mcgraw-Hill Book Company (1993).
  5. Liu, P.: Multi-attribute decision-making method research based on interval vague set and TOPSIS method. Technological and economic development of economy, vol. 15(3), pp. 453-463 (2009). https://doi.org/10.3846/1392-8619.2009.15.453-463
  6. Rezaei, J.: Best-worst multi-criteria decision-making method. Omega, vol. 53, pp.49-57 (2015). https://doi.org/10.1016/j.omega.2014.11.009
  7. Jiang, H., Zhan, J., Sun, B., Alcantud, J. C. R.: A MADM approach to covering-based variable precision fuzzy rough sets: an application to medical diagnosis. International Journal of Machine Learning and Cybernetics, vol. 11(9), pp. 2181-2207 (2020). https://doi.org/10.1007/s13042-020-01109-3
  8. Liu, P., Xu, H., Geng, Y.: Normal wiggly hesitant fuzzy linguistic power Hamy mean aggregation operators and their application to multi-attribute decision-making. Computers & Industrial Engineering, vol. 140, pp. 106224 (2020).
  9. Mousavi, M. M., Lin, J.: The application of PROMETHEE multi-criteria decision aid in financial decision making: Case of distress prediction models evaluation. Expert Systems with Applications, vol. 159, pp. 113438 (2020).
  10. Tang, H., Shi, Y., Dong, P.: Public blockchain evaluation using entropy and TOPSIS. Expert Systems with Applications, vol. 117, pp. 204-210 (2019). https://doi.org/10.1016/j.eswa.2018.09.048
  11. Guo, S., Zhao, H.: Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems, vol. 121, pp. 23-31 (2017).
  12. Zavadskas, E. K., Kaklauskas, A., Turskis, Z., Tamosaitiene, J.: Multi-attribute decision-making model by applying grey numbers. Informatica. vol. 20(2), pp. 305-320 (2009). https://doi.org/10.15388/Informatica.2009.252
  13. Lotfi, F. H., Fallahnejad, R., Navidi, N.: Ranking efficient units in DEA by using TOPSIS method. Applied Mathematical Sciences, vol. 5(17), pp. 805-815 (2011).
  14. Pei, Z., Zheng, L.: A novel approach to multi-attribute decision-making based on intuitionistic fuzzy sets. Expert Systems with Applications, vol. 39(3), pp. 2560-2566 (2012). https://doi.org/10.1016/j.eswa.2011.08.108
  15. Yoon, K. P., Hwang, C. L.: Multiple Attribute Decision Making. Springer, Berlin (1981).
  16. Yoon K.: A reconciliation among discrete compromise solutions. Journal of the Operational Research Society, vol. 38(3), pp. 277-286 (1987). https://doi.org/10.2307/2581948
  17. Duckstein, L., Opricovic, S.: Multiobjective optimization in river basin development. Water resources research, vol. 16(1), pp. 14-20 (1980). https://doi.org/10.1029/WR016i001p00014
  18. Opricovic, S.: Multicriteria optimization of civil engineering systems. Faculty of civil engineering, Belgrade, vol. 2(1), pp. 5-21 (1998).
  19. Julong, D.: Introduction to grey system theory. The Journal of grey system, vol. 1(1), pp. 1-24 (1989).
  20. Yager, R.: On ordered weighted averaging aggregation operators in multi-criteria decision-making. IEEE Transactions on systems", Man, and Cybernetics, vol. 18(1), pp. 183-190 (1988). https://doi.org/10.1109/21.87068
  21. Yakowitz, D. S., Lane, L. J., Szidarovszky, F.: Multi-attribute decision making: dominance with respect to an importance order of the attributes. Applied Mathematics and Computation, vol. 54(2-3), pp. 167-181 (1993). https://doi.org/10.1016/0096-3003(93)90057-L
  22. How to make a decision: the analytic hierarchy process. European Journal of operational research, vol. 48(1), pp. 9-26 (1990). https://doi.org/10.1016/0377-2217(90)90057-I
  23. Saaty T.: What is the analytic hierarchy process?. In Mathematical models for decision support (pp. 109-121). Springer, Berlin, Heidelberg (1988).
  24. Yingming, W.: Using the method of maximizing deviation to make decision for multiindices. Journal of Systems Engineering and Electronics. vol. 8(3), pp. 21-26 (1997).
  25. Zanakis, S. H., Solomon, A., Wishart, N., Dublish, S.: Multi-attribute decision making: A simulation comparison of select methods. European Journal of operational research, vol. 107(3), pp. 507-529 (1998). https://doi.org/10.1016/S0377-2217(97)00147-1
  26. Kersuliene, V., Zavadskas, E. K., Turskis, Z.: Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (SWARA). Journal of business economics and management, vol. 11(2), pp. 243-258 (2010).
  27. Liao, H., & Xu, Z.: Multi-criteriaMulti-criteria decision-making with intuitionistic fuzzy PROMETHEE. Journal of Intelligent & Fuzzy Systems, vol. 27(4), pp. 1703-1717 (2014). https://doi.org/10.3233/IFS-141137
  28. Tosun, O., Akyuz, G. : A fuzzy TODIM approach for the supplier selection problem. International Journal of Computational Intelligence Systems, vol. 8(2),pp. 317-329 (2015). https://doi.org/10.1080/18756891.2015.1001954
  29. Xie, J., Yang, M., Li, J., & Zheng, Z.: Rule acquisition and optimal scale selection in multi-scale formal decision contexts and their applications to smart city. Future Generation Computer Systems, vol. 83, pp. 564-581 (2018).
  30. Gupta, P., Mehlawat, M. K., Grover, N., Pedrycz, W.: Multi-attribute group decision-making based on extended TOPSIS method under interval-valued intuitionistic fuzzy environment. Applied Soft Computing, vol. 69, pp. 554-567 (2018). https://doi.org/10.1016/j.asoc.2018.04.032
  31. Wang, C., Wang, Y., Shao, M., Qian, Y., & Chen, D.: Fuzzy rough attribute reduction for categorical data. IEEE Transactions on Fuzzy Systems, vol. 28(5), pp. 818-830 (2019).