HIVE Weighting: A Novel Heterogeneity-Aware Approach for Criteria Weighting in Multi-Criteria Decision Making
Objective criterion weighting plays an important role in multi-criteria decision-making (MCDM), particularly when decision outcomes depend on how differences among criteria are represented. However, conventional objective weighting approaches may emphasize specific statistical characteristics of the decision matrix without explicitly considering criterion heterogeneity as an integrated concept. This study proposes Heterogeneity-Informed Value Emphasis (HIVE) Weighting, a Heterogeneity-Informed Value Emphasis approach, for objective criterion weighting in MCDM. The method incorporates the mean, standard deviation, and relative variability of normalized criterion values to construct a heterogeneity-based measure of criterion importance. The resulting weights are then integrated with established MCDM methods to examine their applicability in alternative evaluation. A comparative analysis is conducted against Entropy, WENSLO, LOGSTA, CRITIC, and MEREC, followed by evaluations using the SAW and MOORA methods. Sensitivity analysis is further performed by varying criterion weights across multiple perturbation scenarios to examine the stability of the resulting rankings. The findings indicate that HIVE generates a more balanced representation of criterion importance than several comparative weighting approaches while preserving compatibility with different MCDM aggregation procedures. The ranking results obtained through SAW and MOORA show consistent patterns, while the sensitivity analysis indicates that the overall ranking structure is relatively stable under changes in criterion weights. These results demonstrate that HIVE provides an interpretable and flexible framework for incorporating criterion heterogeneity into objective weighting. The proposed approach offers a basis for further research on heterogeneity-aware weighting across diverse MCDM datasets and decision-making contexts, supporting more reliable and transparent weighting practices in complex decision environments.
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References
[1] S. K. Sahoo and S. S. Goswami, “A Comprehensive Review of Multiple Criteria Decision-Making (MCDM) Methods: Advancements, Applications, and Future Directions,” Decis. Mak. Adv., vol. 1, no. 1, pp. 25–48, Dec. 2023, doi: 10.31181/dma1120237.
[2] D. Pamucar, V. Simic, Ö. F. Görçün, and H. Küçükönder, “Selection of the best Big Data platform using COBRAC-ARTASI methodology with adaptive standardized intervals,” Expert Syst. Appl., vol. 239, p. 122312, 2024, doi: 10.1016/j.eswa.2023.122312.
[3] D. D. Trung, A. Ašonja, D. Van Duc, N. C. Bao, and N. H. Son, “Comparison of RAWEC and AROMAN Methods in Material Selection for Manufacturing or Maintenance,” in International Conference on Organization and Technology of Maintenance, 2025, pp. 190–200. doi: 10.1007/978-3-031-80597-4_15.
[4] R. JeddouBen Jeddou, A. Ati, and P. Bouchet, “A Data-Driven Multi-criteria Framework for Recruiting Football Forwards Using CRITIC and TOPSIS,” Oper. Res. Forum, vol. 7, no. 3, p. 102, 2026, doi: 10.1007/s43069-026-00692-5.
[5] A. Puška, D. Božanić, Z. Mastilo, and D. Pamučar, “Extension of MEREC-CRADIS methods with double normalization-case study selection of electric cars,” Soft Comput., vol. 27, no. 11, pp. 7097–7113, 2023, doi: 10.1007/s00500-023-08054-7.
[6] A. Dutta, M. Banerjee, and R. Ray, “Socio-economic backwardness assessment modelling in different gram panchayats of Sali watershed using multi-criteria based weighting and ranking methods,” GeoJournal, vol. 90, no. 1, p. 28, 2025, doi: 10.1007/s10708-024-11276-3.
[7] A. Katrancı, N. Kundakcı, and K. Arman, “Fuzzy SIWEC and Fuzzy RAWEC Methods for Sustainable Waste Disposal Technology Selection,” Spectr. Oper. Res., vol. 3, no. 1 SE-Articles, pp. 87–102, Apr. 2025, doi: 10.31181/sor31202633.
[8] P. Mandal, L. Mrsic, A. Kalampakas, T. Allahviranloo, and S. Samanta, “Pythagorean linguistic information-based green supplier selection using quantum-based group decision-making methodology and the MULTIMOORA approach,” Artif. Intell. Rev., vol. 58, no. 7, p. 199, 2025, doi: 10.1007/s10462-025-11205-x.
[9] K. Yu, Q. Wu, X. Chen, W. Wang, and A. Mardani, “An integrated MCDM framework for evaluating the environmental, social, and governance (ESG) sustainable business performance,” Ann. Oper. Res., vol. 342, no. 1, pp. 987–1018, 2024, doi: 10.1007/s10479-023-05616-8.
[10] I. Mukhametzyanov, “Specific character of objective methods for determining weights of criteria in MCDM problems: Entropy, CRITIC and SD,” Decis. Mak. Appl. Manag. Eng., vol. 4, no. 2, pp. 76–105, Oct. 2021, doi: 10.31181/dmame210402076i.
[11] S. Kousar, A. Ansar, N. Kausar, and G. Freen, “Multi-Criteria Decision-Making for Smog Mitigation: A Comprehensive Analysis of Health, Economic, and Ecological Impacts,” Spectr. Decis. Mak. Appl., vol. 2, no. 1 SE-Articles, pp. 53–67, Jan. 2025, doi: 10.31181/sdmap2120258.
[12] 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.
[13] E. Ibazebo, V. Savsani, A. Siddhpura, and M. Siddhpura, “Developing a Framework for Assessing Boat Collision Risks Using Fuzzy Multi-Criteria Decision-Making Methodology,” Journal of Marine Science and Engineering, vol. 13, no. 9. 2025. doi: 10.3390/jmse13091816.
[14] 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.
[15] E. Roszkowska, “Entropy and Normalization in MCDA: A Data-Driven Perspective on Ranking Stability,” Entropy, vol. 28, no. 1. p. 114, 2026. doi: 10.3390/e28010114.
[16] 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.
[17] M. S. Saidin, L. S. Lee, S. M. Marjugi, M. Z. Ahmad, and H.-V. Seow, “Fuzzy Method Based on the Removal Effects of Criteria (MEREC) for Determining Objective Weights in Multi-Criteria Decision-Making Problems,” Mathematics, vol. 11, no. 6, p. 1544, Mar. 2023, doi: 10.3390/math11061544.
[18] S. Soyguder and G. G. Dinler, “Comparative Analysis of Robotic and Automatic Warehousing Systems with Fuzzy-Based Multi-Criteria Decision Making,” Int. J. Fuzzy Syst., 2026, doi: 10.1007/s40815-026-02249-4.
[19] Do Duc Trung, Nazlı Ersoy, Tran Van Dua, Duong Van Duc, and Manh Thi Diep, “M-OPARA: A Modified Approach to the OPARA Method,” Decis. Mak. Appl. Manag. Eng., vol. 8, no. 1 SE-Regular articles, pp. 256–275, Feb. 2025, doi: 10.31181/dmame8120251334.
[20] F. X. R. S. Wibowo and N. P. Pramesti, “A Systematic Literature Review of Hybrid Multi-Criteria Decision-Making and Technique for Order Preference by Similarity to Ideal Solution Methods for Construction Material Supplier Selection,” Civ. Sustain. Urban Eng., vol. 6, no. 1 SE-Review, p. 48−62, Feb. 2026, doi: 10.53623/csue.v6i1.918.
[21] 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.
[22] C.-N. Wang, N.-A.-T. Nguyen, and T.-T. Dang, “Sustainable Evaluation of Major Third-Party Logistics Providers: A Framework of an MCDM-Based Entropy Objective Weighting Method,” Mathematics, vol. 11, no. 19. p. 4203, 2023. doi: 10.3390/math11194203.
[23] D. T. Do, “Assessing the Impact of Criterion Weights on the Ranking of the Top Ten Universities in Vietnam,” Eng. Technol. Appl. Sci. Res., vol. 14, no. 4, pp. 14899–14903, Aug. 2024, doi: 10.48084/etasr.7607.
[24] Ö. Işık, M. Shabir, and S. Moslem, “A hybrid MCDM framework for assessing urban competitiveness: A case study of European cities,” Socioecon. Plann. Sci., vol. 96, p. 102109, 2024, doi: 10.1016/j.seps.2024.102109.
[25] 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.
[26] 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.
[27] Z. Xiang and X. Zhang, “An integrated decision support system for supplier selection and performance evaluation in global supply chains,” Appl. Soft Comput., vol. 180, p. 113325, 2025, doi: 10.1016/j.asoc.2025.113325.
[28] S. Hashemkhani Zolfani, “Objective Weighting in MCDM: A Comparative Study of CRITIC, ITARA, MEREC, and Beyond,” Decis. Mak. Artif. Intell. Trends, vol. 1, no. 1, pp. 105–120, 2024, doi: 10.22034/dmait.2024.488347.1007.
[29] M. N. Dwi Satria, E. R. Susanto, Setiawansyah, S. Maryana, and P. Palupiningsih, “Modification of Grey Relational Analysis for Dynamic Criteria Weighting in Decision-Making Systems,” IIUM Eng. J., vol. 26, no. 2, pp. 187–203, May 2025, doi: 10.31436/iiumej.v26i2.3494.
[30] 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.
[31] 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.
[32] Z. Stevic, A. Ulutas, A. Topal, D. Marinkovic, and S. Cavoski, “A New Objective Method for Determining Criteria Weights in MCDM Models–Logsta,” Int. J. Simul. Model., vol. 24, no. 4, 2025, doi: 10.2507/IJSIMM24-4-738.
[33] M. Vladimir, Stajić Ljubiša, Mitrović Goran, Novarlić Boris, and Radojičić Zoran, “A Novel Integrated Subjective-Objective MCDM Model for Alternative Ranking in Order to Achieve Business Excellence and Sustainability,” Symmetry (Basel)., vol. 12, no. 1, p. 164, Jan. 2020, doi: 10.3390/sym12010164.
[34] D. Kumar Behera and S. Beura, “Supplier selection for an industry using MCDM techniques,” Mater. Today Proc., vol. 74, pp. 901–909, 2023, doi: 10.1016/j.matpr.2022.11.291.
[35] 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.
[36] R. M. Faisal and Z. N. Shehab, “A Spatial Decision-Support Approach to Identifying Optimal Sites for Rainwater Harvesting,” Trans. GIS, vol. 30, no. 1, p. e70178, Feb. 2026, doi: 10.1111/tgis.70178.
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