Performance Comparison of Multi-Criteria Decision-Making Methods in Decision Support Systems
Keywords:
CRISUS, Multi-Criteria Decision-Making, Comparison, WASPAS, Spearman Rank CorrelationAbstract
This study proposes an integrated Multi-Criteria Decision-Making (MCDM) framework based on the CRISUS weighting method and five ranking approaches, namely Simple Additive Weighting (SAW), Multi-Objective Optimization on the basis of Ratio Analysis (MOORA), Weighted Aggregated Sum Product Assessment (WASPAS), Grey Relational Analysis (GRA), and Multi-Attribute Utility Theory (MAUT), for evaluating and ranking alternatives across multiple criteria. The study aims to assess the consistency and robustness of alternative rankings when different MCDM methods are applied using the same decision matrix and criterion weights. CRISUS is first used to determine the relative importance of the evaluation criteria, and the resulting weights are subsequently incorporated into each ranking method to calculate final preference scores and rankings. The results indicate that Alternative-BM and Alternative-AV consistently achieve the highest rankings across the evaluated methods, while Alternative-JI and Alternative-AR remain among the lowest-ranked alternatives. Although several differences occur in the middle-ranking positions, the overall ranking patterns remain highly consistent. Spearman Rank Correlation analysis confirms this consistency, with SAW, MOORA, WASPAS, and MAUT obtaining correlation coefficients of 0.972, while GRA obtains 0.965. These results indicate a very strong positive relationship between the reference ranking and the rankings produced by the evaluated methods. Therefore, the proposed CRISUS-based MCDM framework demonstrates strong ranking stability and robustness and can provide a reliable basis for multi-criteria evaluation and decision-making.
Downloads
References
[1] S. Dündar, “Prioritizing the Regional Preferences of Turkish Investors Regarding Foreign Direct Investment by ARLON Method,” Konya J. Eng. Sci., vol. 13, no. 3, pp. 927–946, 2025, doi: 10.36306/konjes.1648279.
[2] A. Ajithkumar and P. GaneshKumar, “A greener approach for selecting organic PCM in solar thermal collectors using the MCDM framework,” Sol. Energy, vol. 305, p. 114283, 2026, doi: 10.1016/j.solener.2025.114283.
[3] K. Aliyeva, A. Aliyeva, R. Aliyev, and M. Özdeşer, “Application of Fuzzy Simple Additive Weighting Method in Group Decision-Making for Capital Investment,” Axioms, vol. 12, no. 8. 2023. doi: 10.3390/axioms12080797.
[4] A. M. Yiğit, “Supplier Selection with AHP, TOPSIS and WASPAS as Multi-criteria Decision-making Techniques: An Application in Office Chairs Industry,” Uluslararası İşletme Bilim. ve Uygulamaları Derg., vol. 5, no. 2, pp. 116–130, 2025.
[5] A. Suryadi, A. Adam Muiz, and A. Alpan Hidayat, “Implementation of a Decision Support System for Selecting the Best Supplier Using the SAW Method,” bit-Tech, vol. 7, no. 3, pp. 928–935, Apr. 2025, doi: 10.32877/bt.v7i3.2255.
[6] D. Abdul, J. Wenqi, A. Tanveer, and I. M. Sharaf, “Analyzing the sustainability factors to prioritize sustainable energy source: A novel integrated MCDM model,” Energy, vol. 337, p. 138637, 2025, doi: 10.1016/j.energy.2025.138637.
[7] Y. Liu, “A novel decision making approach based on Picture Hesitant Fuzzy Environment for optimizing art education strategies,” Sci. Rep., 2026, doi: 10.1038/s41598-026-48827-2.
[8] P. Rani, A. R. Mishra, A. M. Alshamrani, A. F. Alrasheedi, and D. Pamucar, “Adoption of Smart Manufacturing Technologies in Small and Medium Enterprises Using Picture Fuzzy Combinative Distance-Based Assessment Model,” J. Knowl. Econ., 2025, doi: 10.1007/s13132-025-02693-x.
[9] I. Adalar and Ö. IŞIK, “CRiterion Importance Based on SUm of Squares (CRISUS): A Novel Objective Weighting Method and Its Implementation in Multidimensional Sustainability Performance Measurement.,” Econ. Comput. Econ. Cybern. Stud. Res., vol. 59, no. 2, 2025, doi: 10.24818/18423264/59.2.25.13.
[10] S. Bektaş, “Financial Performance Ranking With A New Hybrid Decision Making Model: Application Of The CRISUS-RAWEC Hybrid Model For The Reinsurance Industry,” Gümüşhane Univ. J. Soc. Sci., vol. 17, no. 1, pp. 233–249, 2026.
[11] S. GÜL and S. BEKTAŞ, “ESG Performance Ranking with a Novel Hybrid Decision-Making Model: Application of the CRISUS-PIV Hybrid Model for the Banking Sector,” Üçüncü Sektör Sos. Ekon. Derg., vol. 60, no. 4, pp. 3557–3579, 2025, doi: 10.63556/tisej.2025.1582.
[12] S. Setiawansyah, “Decision Support System for Selecting the Best Outsourcing Employee Using CRISUS and WASPAS,” JEECS (Journal Electr. Eng. Comput. Sci., vol. 11, no. 1, pp. 36–49, 2026, doi: 10.54732/jeecs.v11i1.4.
[13] P. Zandi, M. Ajalli, and N. S. Ekhtiyati, “An extended simple additive weighting decision support system with application in the food industry,” Decis. Anal. J., vol. 14, p. 100553, 2025, doi: 10.1016/j.dajour.2025.100553.
[14] D. Sedghiyan, A. Ashouri, N. Maftouni, Q. Xiong, E. Rezaee, and S. Sadeghi, “RETRACTED: Prioritization of renewable energy resources in five climate zones in Iran using AHP, hybrid AHP-TOPSIS and AHP-SAW methods,” Sustain. Energy Technol. Assessments, vol. 44, p. 101045, Apr. 2021, doi: 10.1016/j.seta.2021.101045.
[15] M. Bharadwaj. L, R. Sankargupta, R. Madurai Elavarasan, E. Devaraj, and A. Nandagopal, “Multi-Criteria Decision Analysis framework: Transitioning from coal to Large-Scale Photovoltaics in Australia for achieving SDG 7,” Appl. Energy, vol. 405, p. 127208, 2026, doi: 10.1016/j.apenergy.2025.127208.
[16] A. R. Isnain and Y. Rahmanto, “Employee Performance Evaluation Using the Standard Method of Deviation Multi-Objective Optimization by Ratio Analysis,” J. Inf. Technol. Softw. Eng. Comput. Sci., vol. 2, no. 4, pp. 202–210, Oct. 2024, doi: 10.58602/itsecs.v2i4.164.
[17] I. Tronnebati, F. Jawab, Y. Frichi, and J. Arif, “Green Supplier Selection Using Fuzzy AHP, Fuzzy TOSIS, and Fuzzy WASPAS: A Case Study of the Moroccan Automotive Industry,” Sustainability, vol. 16, no. 11. pp. 1–22, 2024. doi: 10.3390/su16114580.
[18] N. Hendrastuty, S. Setiawansyah, M. G. An’ars, F. A. Rahmadianti, V. H. Saputra, and M. Rahman, “G2M weighting: a new approach based on multi-objective assessment data (case study of MOORA method in determining supplier performance evaluation),” Indones. J. Electr. Eng. Comput. Sci., vol. 38, no. 1, pp. 403–416, 2025, doi: 10.11591/ijeecs.v38.i1.pp403-416.
[19] H. Lu, Y. Zhao, X. Zhou, and Z. Wei, “Selection of Agricultural Machinery Based on Improved CRITIC-Entropy Weight and GRA-TOPSIS Method,” Processes, vol. 10, no. 2, p. 266, Jan. 2022, doi: 10.3390/pr10020266.
[20] S. Setiawansyah and A. Sulistiyawati, “Penerapan Metode Logarithmic Percentage Change-Driven Objective Weighting dan Multi-Attribute Utility Theory dalam Penerimaan Guru Bahasa Inggris,” J. Artif. Intell. Technol. Inf., vol. 2, no. 2, pp. 62–75, 2024, doi: 10.58602/jaiti.v2i2.119.
[21] A. Yudhistira, S. Setiawansyah, T. Ardiansah, S. Maryana, Y. Yadin, and R. Oktaviani, “Development of Multi-Attribute Utility Theory Methods in Dynamic Decision Models Using Change-Data Driven,” Evergreen, vol. 11, no. 4, pp. 3279–3289, Dec. 2024, doi: 10.5109/7326962.
[22] D. A. Megawaty, H. Sulistiani, S. Setiawansyah, A. Qur’Ania, Y. Rahmanto, and P. Palupiningsih, “Modification of the Complex Proportional Assessment Method: A New Methodology for Decision Support,” Evergreen, vol. 13, no. 1, pp. 229–239, 2026, doi: 10.5109/7411063.
Downloads
PUBLISHED DATE SCHEDULE
Issue
Section
License
Copyright (c) 2026 Mahardika Inra Takaendengan (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
