Comparative Evaluation of Ratio-Based MCDM Methods: Performance and Ranking Stability under DAM Weighting
Ratio-based Multi-Criteria Decision-Making (MCDM) methods provide practical approaches for ranking alternatives, but differences in normalization and aggregation mechanisms can produce different rankings and levels of stability. This study comparatively evaluates ratio-based MCDM methods under a common Data Assessment Method (DAM) weighting framework, focusing on MOORA and ARAS for logistics hub location selection. The study uses a dataset comprising 12 candidate logistics hub locations and six criteria. DAM is first applied to derive criterion weights, after which MOORA and ARAS are implemented using the same decision matrix and weighting configuration. Ranking performance is examined through baseline ranking comparison, while robustness is assessed using systematic criterion-weight perturbations of ±0.05 and ±0.10. Kendall’s Tau is employed to measure ranking consistency, and Mean Absolute Rank Change (MARC) is used to evaluate ranking stability. The DAM results show a relatively balanced criterion-weight distribution, with CLH02 receiving the highest weight of 0.1932. Both methods identify LH6 as the best alternative and LH9 as the least preferred, although several intermediate alternatives exchange positions. MOORA achieves a Kendall’s Tau of 0.9163 and a MARC of 0.3715, whereas ARAS obtains 0.9674 and 0.1840, respectively. These results demonstrate that ARAS provides higher ranking consistency and substantially greater stability under weight perturbations. Therefore, ARAS is identified as the better-performing ratio-based MCDM method for the evaluated logistics hub location problem. The findings highlight the importance of assessing both ranking consistency and stability when selecting MCDM methods for robust decision support.
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[1] M. Babanejadi Sorkhab, A. Bose, J. D. Murphy, and R. O’Shea, “A critical review of the application of multi-criteria decision-making in the sustainability assessment of biorefineries,” Biomass and Bioenergy, vol. 210, p. 109036, 2026, doi: 10.1016/j.biombioe.2026.109036.
[2] H. R. Kolour, V. Momayezi, and F. Momayezi, “Enhancing supplier selection in public manufacturing: A hybrid multi-criteria decision-making approach,” Spectr. Decis. Mak. Appl., vol. 3, no. 1, pp. 1–20, 2026, doi: 10.31181/sdmap31202629.
[3] H. F. M.Salih, Z. A. Ameen, B. Alharbi, and B. A. Asaad, “An integrated VIKOR–AHP method for green energy systems based on q-fractional hesitant fuzzy multi-criteria decision-making,” Sci. Rep., vol. 16, no. 1, p. 10618, 2026, doi: 10.1038/s41598-026-46076-x.
[4] M. Şahin, “An integrated multi-criteria decision-making approach for facility layout problems,” J. Supercomput., vol. 82, no. 4, p. 213, 2026, doi: 10.1007/s11227-026-08278-y.
[5] M. Bohanec, N. Kadoić, and N. Begičević Ređep, “Preferential knowledge for multi-criteria decision making: Stability and consistency of decision rules and weights,” EURO J. Decis. Process., vol. 14, p. 100067, 2026, doi: 10.1016/j.ejdp.2025.100067.
[6] A. Sarkar, S. S. Goswami, D. K. Behera, and D. Bozanic, “Criteria Weighting Methods in Multi-Criteria Decision Making: A Comprehensive Review of Subjective, Objective, and Hybrid Approaches,” J. Contemp. Decis. Sci., vol. 2, no. 1, pp. 1–34, 2026, doi: 10.67334/cds202619.
[7] J. Guliyev and M. Deveci, “A stochastic approach to criterion weighting in multi-criteria decision making: Entropy-based Monte Carlo modeling,” Meas. Digit., vol. 7, p. 100043, 2026, doi: 10.1016/j.meadig.2026.100043.
[8] T. Gallozzi, F. Micangeli, D. Bricca, D. Groppi, and D. Astiaso Garcia, “Multi-Criteria Decision-Making for Distributed Renewable Energy Systems: A Review of Methods, Criteria Selection and Weighting Techniques,” Energies, vol. 19, no. 12. p. 2810, 2026. doi: 10.3390/en19122810.
[9] M. Naeimi, M. Akbari, M. J. Nahvinia, and M. Mardian, “A GIS-based MCDM approach to achieve optimal cropping pattern (theory and application in a semi-arid region),” Sci. Rep., vol. 16, no. 1, p. 3599, 2026, doi: 10.1038/s41598-025-33496-4.
[10] M. Gül and E. Akyüz, “Consensus-based GIS–MCDM model for hydrogen infrastructure in Southern Marmara, Türkiye,” Int. J. Hydrogen Energy, vol. 202, p. 152954, 2026, doi: 10.1016/j.ijhydene.2025.152954.
[11] D. Tešić, D. Božanić, S. P. Mondal, and A. Puška, “Modification of the Ranking of Alternatives with Weights of Criterion (RAWEC) Method and Improvement with Fermatean Fuzzy numbers,” J. Soft Comput. Decis. Anal., vol. 3, no. 1, pp. 146–157, Aug. 2025, doi: 10.31181/jscda31202570.
[12] N. F. F. M. Fauzi, Z. M. Rodzi, Z. F. Zainuddin, N. Mustapha, and F. Al-Sharqi, “A New Method for Multi-Criteria Decision-Making: Adaptive Ranking with Ideal Evaluation (ARIE),” Eur. J. Pure Appl. Math., vol. 18, no. 4, p. 6578, 2025, doi: 10.29020/nybg.ejpam.v18i4.6578.
[13] W. Wang, Y. Chen, Y. Wang, M. Deveci, A. Al-Hinai, and S. Kadry, “Analyzing the barriers to blockchain adoption in supply chain finance using an integrated interval-valued Fermatean fuzzy RAFSI model,” Financ. Innov., vol. 11, no. 1, p. 9, 2025, doi: 10.1186/s40854-024-00670-y.
[14] 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.
[15] S. Sintaro, P. Palupiningsih, and J. Wang, “Comparative Analysis of Objective Weighting Approaches in Multi-Criteria Decision Making Using WASPAS,” J. Decis. Support Syst. Multi-Criteria Decis. Mak., vol. 1, no. 1, pp. 16–42, 2026, doi: 10.67449/jodesma.v1i1.2.
[16] Andris Silitonga and Dyah Ayu Megawaty, “Decision Support System Feasibility for Promotion using the Profile Matching Method,” J. Data Sci. Inf. Syst., vol. 1, no. 2, pp. 50–56, May 2023, doi: 10.58602/dimis.v1i2.46.
[17] P. William, O. J. Oyebode, A. Sharma, N. Garg, A. Shrivastava, and A. Rao, “Integrated Decision Support System for Flood Disaster Management with Sustainable Implementation,” IOP Conf. Ser. Earth Environ. Sci., vol. 1285, no. 1, pp. 12–18, Jan. 2024, doi: 10.1088/1755-1315/1285/1/012015.
[18] S. Malefaki, D. Markatos, A. Filippatos, and S. Pantelakis, “A Comparative Analysis of Multi-Criteria Decision-Making Methods and Normalization Techniques in Holistic Sustainability Assessment for Engineering Applications,” Aerospace, vol. 12, no. 2. p. 100, 2025. doi: 10.3390/aerospace12020100.
[19] S. Zhang and M. O. Esangbedo, “Urban Scenic Spot Activity Center Investment: Strategic Construction Company Selection Using the Grey System-II Thinking Compromise Ranking of Alternatives from Distance to Ideal Solution Multi-Criteria Decision-Making Method,” Systems, vol. 13, no. 1. 2025. doi: 10.3390/systems13010067.
[20] A. Fetanat and M. Tayebi, “Fuzzy set–based decision support system for hydrogen sulfide removal technology selection in natural gas processing: a sustainability and efficiency perspective,” Environ. Monit. Assess., vol. 196, no. 12, p. 1267, 2024, doi: 10.1007/s10661-024-13348-w.
[21] 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.
[22] A. Ulutaş, F. Balo, and A. Topal, “Identifying the Most Efficient Natural Fibre for Common Commercial Building Insulation Materials with an Integrated PSI, MEREC, LOPCOW and MCRAT Model,” Polymers (Basel)., vol. 15, no. 6, p. 1500, Mar. 2023, doi: 10.3390/polym15061500.
[23] P. Rani, A. R. Mishra, E. B. Tirkolaee, A. M. Alshamrani, and A. F. Alrasheedi, “Pythagorean fuzzy comprehensive distance-based ranking approach for assessing industry 4.0 adoption strategies in the automotive manufacturing sector,” Adv. Eng. Informatics, vol. 65, p. 103359, 2025, doi: 10.1016/j.aei.2025.103359.
[24] 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.
[25] M. Kaucic, R. Pelessoni, and F. Piccotto, “An Automated Decision Support System for Portfolio Allocation Based on Mutual Information and Financial Criteria,” Entropy, vol. 27, no. 5. p. 480, 2025. doi: 10.3390/e27050480.
[26] G. Patel, S. Das, and R. Das, “Evaluation of optimal normalization techniques in multi-criteria decision-making to rank CMIP6 climate models,” Theor. Appl. Climatol., vol. 156, no. 7, p. 385, 2025, doi: 10.1007/s00704-025-05617-6.
[27] K. Kara, G. C. Yalçın, V. Simic, Z. Baysal, and D. Pamucar, “The alternative ranking using two-step logarithmic normalization method for benchmarking the supply chain performance of countries,” Socioecon. Plann. Sci., vol. 92, p. 101822, 2024, doi: 10.1016/j.seps.2024.101822.
[28] N. Shen, H. You, J. Li, and H. Qian, “Utilizing the Entropy Weighting Method to Determine Objective Weights in Robot Trajectory Optimization,” in 2024 6th International Conference on Communications, Information System and Computer Engineering (CISCE), May 2024, pp. 251–255. doi: 10.1109/CISCE62493.2024.10653410.
[29] D. A. Megawaty et al., “DAM Weighting: A New Approach to Determining Criteria Weighting in Multi-Criteria Decision Making,” Evergreen, vol. 13, no. 2, pp. 788–800, 2026, doi: 10.5109/7432655.
[30] Z. Turskis and V. Keršulienė, “SHARDA–ARAS: A Methodology for Prioritising Project Managers in Sustainable Development,” Mathematics, vol. 12, no. 2, p. 219, Jan. 2024, doi: 10.3390/math12020219.
[31] Y. Mei and C. Wang, “An Likelihood Interval-Valued Intuitionistic Fuzzy DNMEREC–MULTIMOORA Method and Its Application in Enterprise Green Technology Investment Decision,” Int. J. Fuzzy Syst., vol. 28, no. 1, pp. 297–318, 2026, doi: 10.1007/s40815-024-01952-4.
[32] 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.
[33] M. Baydaş, M. Yılmaz, Ž. Jović, Ž. Stević, S. E. G. Özuyar, and A. Özçil, “A comprehensive MCDM assessment for economic data: success analysis of maximum normalization, CODAS, and fuzzy approaches,” Financ. Innov., vol. 10, no. 1, p. 105, Mar. 2024, doi: 10.1186/s40854-023-00588-x.
[34] F. Sevim and E. U. Aldogan, “Evaluation of Health Systems Performance of OECD Countries Using MOORA Method,” J. Health Manag., vol. 26, no. 1, pp. 172–183, Jan. 2024, doi: 10.1177/09720634231215131.
[35] D. D. Trung, “Application of EDAS, MARCOS, TOPSIS, MOORA and PIV Methods for Multi-Criteria Decision Making in Milling Process,” Strojnícky časopis - J. Mech. Eng., vol. 71, no. 2, pp. 69–84, 2021, doi: 10.2478/scjme-2021-0019.
[36] Desyanti, Tri Handayani, Febrina Sari, Dewi Anjani, Kelik Sussolaikah, and Ade Dwi Putra, “A New Approach to Weight Allocation in Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) for Fair Decision Making,” Evergreen, vol. 12, no. 2, pp. 1226–1237, Jun. 2025, doi: 10.5109/7363506.
[37] 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.
[38] R. Raj, A. Singh, V. Kumar, T. De, and S. Singh, “Assessing the e-commerce last-mile logistics’ hidden risk hurdles,” Clean. Logist. Supply Chain, vol. 10, no. 1, p. 100131, 2024, doi: 10.1016/j.clscn.2023.100131.
[39] M. Sheykhizadeh, R. Ghasemi, H. R. Vandchali, A. Sepehri, and S. A. Torabi, “A hybrid decision-making framework for a supplier selection problem based on lean, agile, resilience, and green criteria: a case study of a pharmaceutical industry,” Environ. Dev. Sustain., vol. 26, no. 12, pp. 30969–30996, 2024, doi: 10.1007/s10668-023-04135-7.
[40] T. Sharma and A. Sarin, “Multi-criteria decision making for solar site selection in Punjab, India: An evaluation of site suitability using hybrid MCDM techniques towards the goal of sustainable energy development,” Results Eng., vol. 27, no. 1, pp. 106–125, 2025, doi: 10.1016/j.rineng.2025.106288.
[41] S. Ascione, U. D. Nielsen, V. Piscopo, and G. Storhaug, “Sea state reconstruction based on the Spearman rank correlation using full-scale data from a containership,” Ocean Eng., vol. 343, p. 123471, 2026, doi: 10.1016/j.oceaneng.2025.123471.
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