CURLIA: A Streamlined Aggregation Method That Elevates Ranking Accuracy in Complex MCDM Problems
Abstract
Multi-Criteria Decision-Making (MCDM) techniques frequently produce inconsistent rankings due to variations among methods, complicating the identification of the most accurate ranking of alternatives. Thus, effectively aggregating these distinct rankings becomes crucial. This research proposes the CURLI-AGGREGATOR (CURLIA) method, a novel aggregation technique developed as an extension of the existing Collaborative Unbiased Rank List integration (CURLI) method. While CURLI ranks alternatives directly based on multiple criteria, CURLIA aggregates rankings previously determined by different MCDM methods. The effectiveness of CURLIA was validated through three case studies, each differing in application domain, alternative count, and MCDM techniques employed. Additionally, an extensive simulation involving 1,000 randomized scenarios was performed to evaluate its robustness. Results from a statistical analysis demonstrated that CURLIA significantly outperformed the widely used COPELAND aggregation method in approximately 78.3% of these scenarios. Thus, the CURLIA method constitutes a reliable and robust aggregation tool, significantly enhancing decision-making accuracy across complex and diverse MCDM environments.
References
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Z. Kılıç and A. İ. Şimşek, “Utilizing Multi-Criteria Decision-Making Techniques in the Selection of Electric Vehicles: An Analysis for the Turkish Electric Vehicle Market,” Fırat Üniversitesi Uluslararası İktisadi ve İdari Bilimler Dergisi, vol. 8, no. 2, pp. 107–130, Dec. 2024, doi: 10.61524/fuuiibfdergi.1574901.
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A. Bączkiewicz, B. Kizielewicz, A. Shekhovtsov, J. Wątróbski, and W. Sałabun, “Methodical Aspects of MCDM Based E-Commerce Recommender System,” Journal of Theoretical and Applied Electronic Commerce Research, vol. 16, no. 6, pp. 2192–2229, Sep. 2021, doi: 10.3390/jtaer16060122.
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R. Akpahou, L. D. Mensah, D. A. Quansah, and F. Kemausuor, “Long-term energy demand modeling and optimal evaluation of solutions to renewable energy deployment barriers in Benin: A LEAP-MCDM approach,” Energy Reports, vol. 12, pp. 1888–1904, Dec. 2024, doi: 10.1016/j.egyr.2024.07.055.
C.-H. Yang, W. Hsu, and Y.-L. Wu, “A hybrid multiple-criteria decision portfolio with the resource constraints model of a smart healthcare management system for public medical centers,” Socio-economic Planning Sciences, vol. 80, p. 101073, Mar. 2022, doi: 10.1016/j.seps.2021.101073.
S. Seker, “Evaluation of agile attributes for low-cost carriers to achieve sustainable development using an integrated MCDM approach,” Management Decision, vol. 63, no. 4, pp. 1229–1261, Mar. 2025, doi: 10.1108/MD-10-2023-1896.
A. I. Marqués, V. García, and J. S. Sánchez, “Ranking-based MCDM models in financial management applications: analysis and emerging challenges,” Progress in Artificial Intelligence, vol. 9, no. 3, pp. 171–193, Sep. 2020, doi: 10.1007/s13748-020-00207-1. J.
Więckowski and W. Sałabun, “Aggregating Multi-Criteria Decision Analysis results with a novel fuzzy ranking approach,” in 2024 IEEE 63rd Conference on Decision and Control (CDC), IEEE, Dec. 2024, pp. 2958–2963. doi: 10.1109/CDC56724.2024.10886467.
H.-J. Shyur and H.-S. Shih, “Resolving Rank Reversal in TOPSIS: A Comprehensive Analysis of Distance Metrics and Normalization Methods,” Informatica, pp. 837–858, Nov. 2024, doi: 10.15388/24-INFOR576.
M. Baydaş, T. Eren, Ž. Stević, V. Starčević, and R. Parlakkaya, “Proposal for an objective binary benchmarking framework that validates each other for comparing MCDM methods through data analytics,” PeerJ Computer Science, vol. 9, p. e1350, Apr. 2023, doi: 10.7717/peerj-cs.1350.
M. Mohammadi and J. Rezaei, “Ensemble ranking: Aggregation of rankings produced by different multi-criteria decision-making methods,” Omega (Westport), vol. 96, p. 102254, Oct. 2020, doi: 10.1016/j.omega.2020.102254.
H. Taherdoost, “Data Collection Methods and Tools for Research; A Step-by-Step Guide to Choose Data Collection Technique for Academic and Business Research Projects,” International Journal of Academic Research in Management (IJARM), vol. 10, no. 1, pp. 1038, 2021.
M. Mohammadi and J. Rezaei, “Ensemble ranking: Aggregation of rankings produced by different multi-criteria decision-making methods,” Omega (Westport), vol. 96, p. 102254, Oct. 2020, doi: 10.1016/j.omega.2020.102254. preprint
P. Lederer, D. Peters, and T. Wąs, “The Squared Kemeny Rule for Averaging Rankings,” arXiv arXiv:2404.08474, https://arxiv.org/abs/2404.08474. 2024. [Online]. Available:
S. D. Dvoenko and D. O. Pshenichny, “Rank Aggregation Based on New Types of the Kemeny’s Median,” Pattern Recognition and Image Analysis, vol. 31, no. 2, pp. 185–196, Apr. 2021, doi: 10.1134/S1054661821020061.
P. Zaraté and R. G. Rasoanaivo, “A rank aggregating method based on several multi-criteria methodologies ‘minimum of ranks’: a case study for student’s room allocation,” International Journal of Decision Support Systems, vol. 5, no. 1, pp. 75–101, 2024, doi: 10.1504/IJDSS.2024.10062705.
W. H. Holliday, “A Simple Condorcet Voting Method for Final Four Elections,” Representation, pp. 1–24, May 2025, doi: 10.1080/00344893.2025.2473397.
T. Qin, X. Geng, and T.-Y. Liu, “A new probabilistic model for rank aggregation,” in Advances in Neural Information Processing Systems, J. Lafferty, Ed. 2010, pp. 1–9.
F. Franceschini and D. Maisano, “Design decisions: concordance of designers and effects of the Arrow’s theorem on the collective preference ranking,” Research in Engineering Design, vol. 30, no. 3, pp. 425–434, Jul. 2019, doi: 10.1007/s00163-019-00313-9.
N. Boehmer, R. Bredereck, and D. Peters, “Rank Aggregation Using Scoring Rules,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 5, pp. 5515–5523, Jun. 2023, doi: 10.1609/aaai.v37i5.25685.
R. E. Hodgett, “Comparison of multi-criteria decision-making methods for equipment selection,” The International Journal of Advanced Manufacturing Technology, vol. 85, no. 58, pp. 1145–1157, Jul. 2016, doi: 10.1007/s00170-015-7993-2.
J. Więckowski and W. Sałabun, “Aggregating Multi-Criteria Decision Analysis results with a novel fuzzy ranking approach,” in 2024 IEEE 63rd Conference on Decision and Control (CDC), IEEE, Dec. 2024, pp. 2958–2963. doi: 10.1109/CDC56724.2024.10886467.
B. Bepari, B. Bairagi, H. S. Das, and G. Roymahapatra, “De-novo Approach in Rank Agglomeration in Multi Criteria Decision Making for Materials Selection,” ES General, 2024, doi: 10.30919/esg1353.
U. Grandi, A. Loreggia, F. Rossi, and V. Saraswat, “A Borda count for collective sentiment analysis,” Annals of Mathematics and Artificial Intelligence, vol. 77, no. 3–4, pp. 281–302, Aug. 2016, doi: 10.1007/s10472-015-9488-0.
S. Abacıoğlu, B. Ayan, and D. Pamucar, “The Race to Sustainability: Decoding Green University Rankings Through a Comparative Analysis (2018–2022),” Innovative Higher Education, vol. 50, no. 1, pp. 241–275, Feb. 2025, doi: 10.1007/s10755-024-09734-4.
D. G. Saari, “Selecting a voting method: the case for the Borda count,” Constitutional Political Economy, vol. 34, no. 3, pp. 357–366, Sep. 2023, doi: 10.1007/s10602-022-09380-y.
J. Fraenkel and B. Grofman, “The Borda Count and its real-world alternatives: Comparing scoring rules in Nauru and Slovenia,” Australian Journal of Political Science, vol. 49, no. 2, pp. 186–205, Apr. 2014, doi: 10.1080/10361146.2014.900530.
Ö. Işık, A. Çalık, and M. Shabir, “A Consolidated MCDM Framework for Overall Performance Assessment of Listed Insurance Companies Based on Ranking Strategies,” Computational Economics, vol. 65, no. 1, pp. 271–312, Jan. 2025, doi: 10.1007/s10614-02410578-5.
Z. Qiu, M. G. Dosskey, and Y. Kang, “Choosing between alternative placement strategies for conservation buffers using Borda count,” Landscape and Urban Planning, vol. 153, pp. 6673, Sep. 2016, doi: 10.1016/j.landurbplan.2016.03.012.
Z. Kılıç and A. İ. Şimşek, “Utilizing Multi-Criteria Decision-Making Techniques in the Selection of Electric Vehicles: An Analysis for the Turkish Electric Vehicle Market,” Fırat Üniversitesi Uluslararası İktisadi ve İdari Bilimler Dergisi, vol. 8, no. 2, pp. 107–130, Dec. 2024, doi: 10.61524/fuuiibfdergi.1574901.
S. Karakaş Geyik, M. Satman, and G. Kalyoncu, “G20 Ülkelerinin Covid-19 Pandemisi ile Mücadele Performanslarının Çok Kriterli Karar Verme Yöntemleri ile Değerlendirilmesi,” Ekoist: Journal of Econometrics and Statistics, vol. 0, no. 37, pp. 27–52, Dec. 2022, doi: 10.26650/ekoist.2022.37.1161945.
H. Naderi, H. Shahosseini, and A. Jafari, “Evaluation MCDM Multi-disjoint Paths Selection Algorithms Using Fuzzy-Copeland Ranking Method,” International Journal of Communication Networks and Information Security (IJCNIS), vol. 5, no. 1, Apr. 2022, doi: 10.17762/ijcnis.v5i1.288.
N. Ersoy and S. Taslak, “Comparative Analysis of MCDM Methods for the Assessment of Corporate Sustainability Performance in Energy Sector,” Ege Akademik Bakis (Ege Academic Review), Aug. 2022, doi: 10.21121/eab.986122.
F. Aydin and B. Gümüş, “Comparative analysis of multi-criteria decision making methods for the assessment of optimal SVC location,” Bulletin of the Polish Academy of Sciences Technical Sciences, pp. 140555–140555, Feb. 2022, doi: 10.24425/bpasts.2022.140555.
M. Moghimi and M. Taghizadeh Yazdi, “Applying Multi-Criteria Decision-Making (MCDM) Methods for Economic Ranking of Tehran-22 Districts to Establish Financial and Commercial Centers (Case: City of Tehran),” Journal of Urban Economics and Management, vol. 5, no. 20, pp. 39–51, Dec. 2017, doi: 10.29252/iueam.5.20.39.
J. R. Kiger and D. J. Annibale, “A new method for group decision making and its application in medical trainee selection,” Medical Education, vol. 50, no. 10, pp. 1045–1053, Oct. 2016, doi: 10.1111/medu.13112.
A. Bączkiewicz, B. Kizielewicz, A. Shekhovtsov, J. Wątróbski, and W. Sałabun, “Methodical Aspects of MCDM Based E-Commerce Recommender System,” Journal of Theoretical and Applied Electronic Commerce Research, vol. 16, no. 6, pp. 2192–2229, Sep. 2021, doi: 10.3390/jtaer16060122.
C. Kuhlman and E. Rundensteiner, “Rank aggregation algorithms for fair consensus,” Proceedings of the VLDB Endowment, vol. 13, no. 12, pp. 2706–2719, Aug. 2020, doi: 10.14778/3407790.3407855.
R. Kumar and D. Pamucar, “A Comprehensive and Systematic Review of Multi-Criteria Decision-Making (MCDM) Methods to Solve Decision-Making Problems: Two Decades from 2004 to 2024,” Spectrum of Decision Making and Applications, vol. 2, no. 1, pp. 178–197, Jan. 2025, doi: 10.31181/sdmap21202524.
A. Biswas, K. H. Gazi, P. Bhaduri, and S. P. Mondal, “Site Selection for Girls Hostel in a University Campus by MCDM based Strategy,” Spectrum of Decision Making and Applications, vol. 2, no. 1, pp. 68–93, Jan. 2025, doi: 10.31181/sdmap21202511.
A. Shahid, S. Ashraf, and M. S. Chohan, “Complex Fuzzy MARCOS and WASPAS Approaches with Z-Numbers for Augmented Reality Decision Making,” Spectrum of Operational Research, vol. 3, no. 1, pp. 40–62, Mar. 2026, doi: 10.31181/sor31202637.
R. Gul, “An Extension of VIKOR Approach for MCDM Using Bipolar Fuzzy Preference δCovering Based Bipolar Fuzzy Rough Set Model,” Spectrum of Operational Research, vol. 2, no. 1, pp. 72–91, Jan. 2025, doi: 10.31181/sor21202511.
S. Petchimuthu, B. Palpandi, K. Rajakumar, and F. Banu M, “Sustainable Urban Development: q-Rung Orthopair Fuzzy MCDM withGeneralized Power Prioritized Yager Aggregation,” Spectrum of Operational Research, vol. 3, no. 1, pp. 275–309, Jun. 2026, doi: 10.31181/sor31202649.
Published
2025-12-08
How to Cite
TRUNG, Do Duc et al.
CURLIA: A Streamlined Aggregation Method That Elevates Ranking Accuracy in Complex MCDM Problems.
Yugoslav Journal of Operations Research, [S.l.], dec. 2025.
ISSN 2334-6043.
Available at: <https://yujor.fon.bg.ac.rs/index.php/yujor/article/view/1375>. Date accessed: 20 dec. 2025.
doi: https://doi.org/10.2298/YJOR250815040T.
Issue
Section
Research Articles
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
