A scale independent and distribution sensitive approach in multi-criteria decision-making (MCDM): The Canberra distance performance measurement (CADPM) method

Furkan Fahri Altıntaş

doi:10.17093/alphanumeric.1812325

A scale independent and distribution sensitive approach in multi-criteria decision-making (MCDM): The Canberra distance performance measurement (CADPM) method

Abstract

This study introduces the Canberra Distance Performance Measurement (CADPM) method, a scale-independent and distribution-sensitive framework developed to address persistent methodological limitations in Multi-Criteria Decision-Making (MCDM), including linear dependency, scale heterogeneity, and inadequate responsiveness to micro-level variations. CADPM evaluates alternatives through proportional, component-wise comparisons and eliminates the need for predefined criterion weights, as the intrinsic structure of the Canberra metric neutralizes scale effects and preserves relative positional relationships. The study also incorporates the Proximity Index Value (PIV) method to ensure a comprehensive comparative setting and to examine weight-free reference-based evaluation under a unified analytical perspective. The empirical assessment comprises both real-data and simulation-based case studies designed to observe the method’s robustness under heterogeneous distributions. Sensitivity analyses demonstrate that CADPM is strongly resistant to ranking instability and remains unaffected by systematic weight perturbations. Comparative analyses reveal high concordance between CADPM and established techniques such as MARCOS, TOPSIS, CODAS, WEDBA, and PIV, confirming that the method exhibits structural compatibility while preserving its distinctive proportional sensitivity. Simulation experiments further show that CADPM maintains stable performance patterns across diverse scenario structures, underscoring its consistency under controlled variance, scale, and distributional shifts. The primary advantage of CADPM lies in its ability to amplify subtle proportional differences, particularly in small or near-zero values, which conventional absolute-difference metrics often suppress. This capability enables more equitable and discriminative evaluations in asymmetric or heterogeneous datasets. Overall, CADPM offers a theoretically coherent, computationally stable, and empirically validated contribution to MCDM research. Future work may explore parametric extensions, hybrid integrations with reference-based models, and broader applications across complex decision environments.

Keywords

References

Abu Alfeilat, H. A., Hassanat, A. B., Lasassmeh, O., Tarawneh, A. S., Alhasanat, M. B., Eyal Salman, H. S., & Prasath, V. S. (2019). Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review. Big Data, 7(4), 221–248. https://doi.org/10.1089/big.2018.0175
Aksoy Erzurumlu, E., & Gençtürk, M. (2025). A Research on the Financial Performance of Islamic Banks: SD-WEDBA Method. Alanya Akademik Bakış, 9(2), 639–650. https://doi.org/10.29023/alanyaakademik.1622511
Alam Sur, W. A., Adriana, M., Machfiroh, I. S., Dewi, N., Kurniawan, B., & Safitri, I. (2025). COMPARATIVE STUDY OF INTEGRATED MULTICRITERIA DECISION MAKING: AHP-TOPSIS VS ENTROPY-TOPSIS FOR PRIORITIZING ROAD DAMAGE REPAIR. Jurnal Jalan Jembatan, 42(1). https://doi.org/10.58499/jatan.v42i1.1331
Alhadidi, T. I., Awad, F. A., Hindawi, R., & Alazmi, A. (2025). Assessing the operational features of bus rapid transit at innovative roundabouts: an integrated BWM-MABAC method. Innovative Infrastructure Solutions, 10(9). https://doi.org/10.1007/s41062-025-02210-8
Alqoud, A., Milisavljevic-Syed, J., & Salonitis, K. (2025). Multi-criteria decision making in evaluating digital retrofitting solutions: utilising AHP and TOPSIS. Procedia CIRP, 132, 184–190. https://doi.org/10.1016/j.procir.2025.01.031
Andrejić, M., & Pajić, V. (2025). Strengthening the Sustainability and Resilience of Global Supply Chains: An Integrated Risk Assessment Framework for International Logistics Security. Opportunities and Challenges in Sustainability. https://doi.org/https://doi.org/10.56578/ocs040105
Arivendan, A., Chen, X., Zhang, Y.-F., & Gao, W. (2025). Assessing the structural, mechanical, and thermal performance of Linum usitatissimum/carbon fiber composites by using novel WASPAS and CRITIC methods. Industrial Crops and Products, 224, 120378. https://doi.org/10.1016/j.indcrop.2024.120378
Arman, K., Kundakcı, N., & Katrancı, A. (2026). Digital Innovation Performance Evaluation of European Union Member and Candidate Countries with IDOCRIW and CRADIS Methods. Spectrum of Decision Making and Applications, 3(1), 364–382. https://doi.org/10.31181/sdmap31202650

Ashraf, S., Iqbal, W., Hameed, M. S., Simic, V., & Bacanin, N. (2024). An enhanced CRADIS decision model for optimizing radioactive waste reduction through transmutations based on Disc Spherical Fuzzy information. Applied Soft Computing, 167, 112289. https://doi.org/10.1016/j.asoc.2024.112289
Atan, M., & Altan, Ş. (2020). Örnek Uygulamalarla Çok Kriterli Karar Verme Yöntemleri. Gazi Kitapevi.
Barbara, F., Santos, M. dos, Silva, A. S., Moreira, M. Â. L., Fávero, L. P., Pereira Júnior, E. L., Anjos Carvalho, W. dos, Muradas, F. M., Moura Pereira, D. A. de, & Portella, A. G. (2023). Interactive Internet Framework Proposal of WASPAS Method: A Computational Contribution for Decision-Making Analysis. Mathematics, 11(15), 3375. https://doi.org/10.3390/math11153375
Begam, S., & Majumder, M. (2024). Identification of Groundwater recharge zone in peri urban watershed with the help of MAUT and MEREC techniques. https://doi.org/10.21203/rs.3.rs-4178085/v1
Bhola, A., Goswami, S. S., Mondal, S., & Behera, D. K. (2025). Multi-criteria assessment ranking of facade's alternatives using EDAS and CODAS combined MCDM system. Decision Making and Analysis, 24–40. https://doi.org/10.55976/dma.32025138924-40
Borcard, D., Gillet, F., & Legendre, P. (2018). Numerical Ecology with R. Springer International Publishing. https://doi.org/10.1007/978-3-319-71404-2
Boujelben, S., & Souissi, M. (2025). Comparison of a novel single reference point multi-attribute decision making method with EDAS method. International Journal of Operational Research, 52(3), 360–381. https://doi.org/10.1504/ijor.2025.144672
Bridgman, P. W. (1922). Dimensional Analysis. Yale University Press.
Cha, S. H. (2007). Comprehensive survey on distance/similarity measures between probability density functions. International Journal of Mathematical Models and Methods in Applied Sciences, 4(1), 300–307.
Chaipetch, P., Amprayn, C., Pawan, P., & Ratanavaraha, V. (2025). A Multi-Criteria Decision Support System for Prioritizing Road Maintenance: Integrating AHP and TOPSIS with a Focus on Low-Volume Roads. IOP Conference Series: Earth and Environmental Science, 1450(1), 12004. https://doi.org/10.1088/1755-1315/1450/1/012004
Chatterjee, S., & Chakraborty, S. (2021). A multi-attributive ideal-real comparative analysis-based approach for piston material selection. OPSEARCH, 59(1), 207–228. https://doi.org/10.1007/s12597-021-00536-2
Chen, T.-Y. (2025). Enhanced Multi-Attribute Ideal-Real comparative analysis with the circular intuitionistic fuzzy framework: Application to hybrid cloud services. Advanced Engineering Informatics, 65, 103184. https://doi.org/10.1016/j.aei.2025.103184
Cheng, R., Fan, J., Wu, M., & Seiti, H. (2024). A large-scale multi-attribute group decision-making method with R-numbers and its application to hydrogen fuel cell logistics path selection. Complex & Intelligent Systems, 10(4), 5213–5260. https://doi.org/10.1007/s40747-024-01437-9
Churchman, C. W., & Ackoff, R. L. (1954). An Approximate Measure of Value. Journal of the Operations Research Society of America, 2(2), 172–187. https://doi.org/10.1287/opre.2.2.172
Ciardiello, F., & Genovese, A. (2023). A comparison between TOPSIS and SAW methods. Annals of Operations Research, 325(2), 967–994. https://doi.org/10.1007/s10479-023-05339-w
Corrente, S., & Tasiou, M. (2023). A robust TOPSIS method for decision making problems with hierarchical and non-monotonic criteria. Expert Systems with Applications, 214, 119045. https://doi.org/10.1016/j.eswa.2022.119045
Dai, X., Li, H., Zhou, L., & Wu, Q. (2024). The SMAA-MABAC approach for healthcare supplier selection in belief distribution environment with uncertainties. Engineering Applications of Artificial Intelligence, 129, 107654. https://doi.org/10.1016/j.engappai.2023.107654
Demir, G., & Arslan, R. (2022). Sensitivity Analysis in Multi-Criterion Decision-Making Problems. Ankara Hacı Bayram Veli Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 24(3), 1025–1056. https://doi.org/10.26745/ahbvuibfd.1103531
Demir, G., & Kartal, M. (2021). Güncel Çok Kriterli Karar Verme Teknikleri. Akademisyen Kitabevi. https://doi.org/10.37609/akya.808
Demir, G., & Özyalçın, A. T. (2021). Çok Kriterli Karar Verme Yöntemleri ve ÇKKV Yazılımı ile Problem Çözümü. Nobel Akademik Yayıncılık.
Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research, 22(7), 763–770. https://doi.org/10.1016/0305-0548(94)00059-h
Do, D., Ersoy, N., Nguyen, T., & Hoang, X. (2025). Applying MCDM methods for electric vehicle selection: a comparative study between CRADIS and PIV methods. Journal of Applied Engineering Science, 23(2), 345–358. https://doi.org/10.5937/jaes0-56793
Doğruel, M. (2025). Data-Driven Insights into Climate Change and Technological Levels: Data Mining Visualization and TOPSIS Approach. Alphanumeric Journal, 13(1), 13–35. https://doi.org/10.17093/alphanumeric.1647858
Durak, M. Ş. (2025). Corporate sustainability analysis: Evaluating the performance of IGA Istanbul Airport using LOPCOW and MAUT methods. Afyon Kocatepe Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 27(2), 348–368. https://doi.org/10.33707/akuiibfd.1664993
Ecer, F. (2020). Çok Kriterli Karar Verme. Seçkin Yayıncılık.
Ecer, F. (2022). An extended MAIRCA method using intuitionistic fuzzy sets for coronavirus vaccine selection in the age of COVID-19. Neural Computing and Applications, 34(7), 5603–5623. https://doi.org/10.1007/s00521-021-06728-7
Ecer, F., & Pamucar, D. (2022). A novel LOPCOW-DOBI multi-criteria sustainability performance assessment methodology: An application in developing country banking sector. Omega, 112, 102690. https://doi.org/10.1016/j.omega.2022.102690
El-Araby, A., Sabry, I., & El-Assal, A. (2024). Ranking Performance of MARCOS Method for Location Selection Problem in the Presence of Conflicting Criteria. Decision Making Advances, 2(1), 148–162. https://doi.org/10.31181/dma21202435
Emran, S. M., & Ye, N. (2002). Robustness of Chi-square and Canberra distance metrics for computer intrusion detection. Quality and Reliability Engineering International, 18(1), 19–28. https://doi.org/10.1002/qre.441
Faith, D. P., Minchin, P. R., & Belbin, L. (1987). Compositional dissimilarity as a robust measure of ecological distance. Vegetatio, 69(1–3), 57–68. https://doi.org/10.1007/bf00038687
Fan, J., Han, D., & Wu, M. (2023). Picture fuzzy Additive Ratio Assessment Method (ARAS) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method for multi-attribute decision problem and their application. Complex & Intelligent Systems, 9(5), 5345–5357. https://doi.org/10.1007/s40747-023-01007-5
Ghanbari, H., Seiti, H., Mohammadi, E., & Elkamel, A. (2025). Selecting a sustainable hydrogen production method using a novel dual evaluation EDAS approach. Annals of Operations Research. https://doi.org/10.1007/s10479-025-06616-6
Gigović, L., Pamučar, D., Bajić, Z., & Milićević, M. (2016). The Combination of Expert Judgment and GIS-MAIRCA Analysis for the Selection of Sites for Ammunition Depots. Sustainability, 8(4), 372. https://doi.org/10.3390/su8040372
Goswami, S. S., Behera, D. K., & Mitra, D. S. (2020). A Comprehensive Study of Weighted Product Model for Selecting the Best Product in Our Daily Life. Brazilian Journal of Operations & Production Management, 17(2), 1–18. https://doi.org/10.14488/bjopm.2020.017
Goswami, S. S., Tapankumar, T., Naik, N. C. K., Gowrishankar, J., Bhosle, N., Singh, A., Raju, G. S., Nagesh, D., & Santhosh, A. J. (2025). Multi-model MCDM framework for sustainable renewable energy selection in India: integrating CRITIC-EDAS-CODAS-CoCoSo. Discover Sustainability, 6(1). https://doi.org/10.1007/s43621-025-01069-0
Gower, J. C., & Legendre, P. (1986). Metric and Euclidean properties of dissimilarity coefficients. Journal of Classification, 3(1), 5–48. https://doi.org/10.1007/bf01896809
Han, F., Alkhawaji, R. N., & Shafieezadeh, M. M. (2024). Evaluating sustainable water management strategies using TOPSIS and fuzzy TOPSIS methods. Applied Water Science, 15(1). https://doi.org/10.1007/s13201-024-02336-7
Helton, J., & Davis, F. (2003). Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Reliability Engineering & System Safety, 81(1), 23–69. https://doi.org/10.1016/s0951-8320(03)00058-9
Hwang, C.-L., & Yoon, K. (1981). Multiple Attribute Decision Making. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-48318-9
Jafari, M., & Naghdi Khanachah, S. (2024). An integrated multi-attributive border approximation area comparison (MABAC) method for evaluating resilience and knowledge sharing of suppliers in pythagorean fuzzy environment. Artificial Intelligence Review, 57(9). https://doi.org/10.1007/s10462-024-10830-2
Jameel, T., Yasin, Y., & Riaz, M. (2026). An Integrated Hybrid MCDM Framework for Renewable Energy Prioritization in Sustainable Development. Spectrum of Decision Making and Applications, 3(1), 124–150. https://doi.org/10.31181/sdmap31202640
Jurman, G., Riccadonna, S., Visintainer, R., & Furlanello, C. (2009). Canberra Distance on Ranked Lists. in: Advances in Ranking (S. Agarwal, C. Burges, & K. Crammer, Eds.; pp. 22–27). NIPS.
Karadağ, M. M. (2024). Analyzing the Turkish insurance companies' financial performance traded on BIST implementing the critic-based PIV method. Insurance Markets and Companies, 15(2), 47–60. https://doi.org/10.21511/ins.15(2).2024.05
Karaş, Z. (2024). TÜRK BANKACILIK SEKTÖRÜNDE ÇOKLU KRİTERLİ KARAR VERME YAKLAŞIMLARI İLE FİNANSAL PERFORMANS DEĞERLENDİRMESİ. Elektronik Sosyal Bilimler Dergisi, 23(90), 798–820. https://doi.org/10.17755/esosder.1429337
Keeney, R. L., & Raiffa, H. (1976). Decision Making with Multiple Objectives Preferences and Value Tradeoffs. Wiley.
Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2015). Multi-Criteria Inventory Classification Using a New Method of Evaluation Based on Distance from Average Solution (EDAS). Informatica, 26(3), 435–451. https://doi.org/10.15388/informatica.2015.57
Keshavarz Ghorabaee, M., Zavadskas, K., Turskis, Z., & Antucheviciene, J. (2016). A new combinative distance-based assessment (CODAS) method for multi-criteria decision-making. Economic Computation and Economic Cybernetics Studies and Research, 3(50), 26–44.
Kizielewicz, B., Więckowski, J., & Sałabun, W. (2025). Fuzzy normalization-based Multi-Attributive Border Approximation Area Comparison. Engineering Applications of Artificial Intelligence, 141, 109736. https://doi.org/10.1016/j.engappai.2024.109736
Kocher, M., & Savoy, J. (2017). Distance measures in author profiling. Information Processing & Management, 53(5), 1103–1119. https://doi.org/10.1016/j.ipm.2017.04.004
Kuczynski, J., Liu, Z., Lozupone, C., McDonald, D., Fierer, N., & Knight, R. (2010). Microbial community resemblance methods differ in their ability to detect biologically relevant patterns. Nature Methods, 7(10), 813–819. https://doi.org/10.1038/nmeth.1499
Kulkarni, A. J. (2022). Multiple Criteria Decision Making: Techniques, Analysis and Applications. Springer Nature.
Kumar, A., & Rajak, S. (2025). Selection of Bio-implant Materials Based on Metal Additive Manufacturing using Integrated SWARA and WASPAS MCDM Technique. Journal of Materials Engineering and Performance, 34(17), 18766–18786. https://doi.org/10.1007/s11665-024-10545-z
Kumari, A., & Acherjee, B. (2025). Comparative Effectiveness of Data Normalization Methods in ARAS for Multi-Criteria Decision-Making Across Industrial Applications. International Journal of Industrial Engineering: Theory, Applications and Practice, 32(4). https://doi.org/10.23055/IJIETAP.2025.32.4.10995
Lance, G. N., & Williams, W. T. (1966). Computer Programs for Hierarchical Polythetic Classification (``Similarity Analyses''). The Computer Journal, 9(1), 60–64. https://doi.org/10.1093/comjnl/9.1.60
Lance, G. N., & Williams, W. T. (1967). A General Theory of Classificatory Sorting Strategies: 1. Hierarchical Systems. The Computer Journal, 9(4), 373–380. https://doi.org/10.1093/comjnl/9.4.373
Legendre, P., & Legendre, L. (2012). Numerical Ecology, Developments in Environmental Modelling. Elsevier.
Levy, A., Shalom, B. R., & Chalamish, M. (2024). A Guide to Similarity Measures. https://doi.org/10.48550/ARXIV.2408.07706
Lin, K.-Y., & Chang, K.-H. (2025). Integrating TOPSIS and three-dimensional importance-performance analysis to formulate the career competitiveness evaluation model under the MAES. Acta Scientiarum. Technology, 47(1), e71080. https://doi.org/10.4025/actascitechnol.v47i1.71080
Liu, D., Qiao, L., Liu, C., Liu, B., & Liu, S. (2024). Evaluation of Urban Quality Improvement Based on the MABAC Method and VIKOR Method: A Case Study of Shandong Province, China. Sustainability, 16(8), 3308. https://doi.org/10.3390/su16083308
Lopez, L. M., Ishizaka, A., & Qin, J. (2023). Multi Criteria Decision Making Sorting Methods: Applications to Real World. Academic Press.
Lozynskyi, V. (2025). Multi-criteria assessment of coal seams suitability for co-gasification using the preference selection index. Heliyon, 11(10), e43368. https://doi.org/10.1016/j.heliyon.2025.e43368
Lukić, R. (2025). Analysis of climate change performance of G7 countries based on AHP-CODAS methods. The Holistic Approach to Environment, 15(2), 47–56. https://doi.org/10.33765/thate.15.2.1
Madić, M., Radovanović, M., & Manić, M. (2016). Application of the ROV method for the selection of cutting fluids. Decision Science Letters, 245–254. https://doi.org/10.5267/j.dsl.2015.12.001
Maniya, K., & Bhatt, M. (2010). A selection of material using a novel type decision-making method: Preference selection index method. Materials & Design, 31(4), 1785–1789. https://doi.org/10.1016/j.matdes.2009.11.020
Math.NET Numerics. (2025, ). Distance Metrics Reference Documentation. https://numerics.mathdotnet.com/
Mayer, R., Hittmeir, M., & Ekelhart, A. (2024). Distance-based linkage of personal microbiome records for identification and its privacy implications. Computers & Security, 136, 103538. https://doi.org/10.1016/j.cose.2023.103538
Mitra, A. (2021). Grading of Raw Jute Fibres Using Criteria Importance through Intercriteria Correlation (CRITIC) and Range of Value (ROV) Approach of Multi-criteria Decision Making. Journal of Natural Fibers, 19(14), 7517–7533. https://doi.org/10.1080/15440478.2021.1951422
Mufazzal, S., & Muzakkir, S. (2018). A new multi-criterion decision making (MCDM) method based on proximity indexed value for minimizing rank reversals. Computers & Industrial Engineering, 119, 427–438. https://doi.org/10.1016/j.cie.2018.03.045
Mukhametzyanov, I. (2021). Specific character of objective methods for determining weights of criteria in MCDM problems: Entropy, CRITIC and SD. Decision Making: Applications in Management and Engineering, 4(2), 76–105. https://doi.org/10.31181/dmame210402076i
Mukhametzyanov, I. Z., & Pamucar, D. (2025). Equivalence of MCDM Methods and Synthesis of Solution Based on Ratings Obtained in Different Models. Decision Making: Applications in Management and Engineering, 8(2), 1–20. https://doi.org/10.31181/dmame8220251473
Munier, N. (2024). Strategic Approach in Multi-Criteria Decision Making: A Practical Guide for Complex Scenarios. Springer International Publishing. https://doi.org/10.1007/978-3-031-44453-1
Munier, N., Hontoria, E., & Jiménez-Sáez, F. (2019). Strategic Approach in Multi-Criteria Decision Making: A Practical Guide for Complex Scenarios. Springer International Publishing. https://doi.org/10.1007/978-3-030-02726-1
Nasyuha, A. H., Tujantri, H., Veza, O., Nurarif, S., & Chung, M.-Y. (2025). Comparison of WSM and Weight Product Methods with WSM-Score and Vector Approaches. Sinkron, 9(2), 948–956. https://doi.org/10.33395/sinkron.v9i2.14817
Opricovic, S., & Tzeng, G.-H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445–455. https://doi.org/10.1016/s0377-2217(03)00020-1
Pamucar, D. S., Tarle, S. P., & Parezanovic, T. (2018). New hybrid multi-criteria decision-making DEMATEL-MAIRCA model: sustainable selection of a location for the development of multimodal logistics centre. Economic Research-Ekonomska Istraživanja, 31(1), 1641–1665. https://doi.org/10.1080/1331677X.2018.1506706
Pamučar, D., & Ćirović, G. (2015). The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC). Expert Systems with Applications, 42(6), 3016–3028. https://doi.org/10.1016/j.eswa.2014.11.057
Pena, A. S., Ramírez, Y. G., Martinez, I. R., Ramírez, T. V., & Anzardo, L. E. (2025). Multineutrosophic Analysis with the ARAS Method for Selecting Trading Strategies. Neutrosophic Sets and Systems, 89(14), 160–174.
Petrović, N., Jovanović, V., Petrović, M., Nikolić, B., & Mihajlović, J. (2025). Comparative Investigation of Normalization Techniques and Their Influence on MCDM Ranking – A Case Study. Spectrum of Mechanical Engineering and Operational Research, 2(1), 172–190. https://doi.org/10.31181/smeor21202542
Pisal, P., Reddy, K. K., Kishore, J., Jonnalagadda, R. R., Kumar, M., Band, G., & Joshi, B. P. (2025). An integrated TOPSIS and ARAS method multi-criteria decision-making approach for optimizing investment portfolios using goal programming and genetic algorithm model. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-17604-y
Puška, A., Božanić, D., Mastilo, Z., & Pamučar, D. (2023). Extension of MEREC-CRADIS methods with double normalization-case study selection of electric cars. Soft Computing, 27(11), 7097–7113. https://doi.org/10.1007/s00500-023-08054-7
R Documentation. (2025, ). Dist function in R: Computing Canberra, Euclidean, and other distances. https://www.rdocumentation.org/
Ramancha, N. K., & Ballamudi, S. (2023). Application of the EDAS Method in Artificial Intelligence A Comprehensive Analysis. Journal of Artificial Intelligence and Machine Learning, 1(2), 1–12. https://doi.org/10.55124/jaim.v1i2.254
Rao, R., & Singh, D. (2012). Weighted Euclidean distance based approach as a multiple attribute decision making method for plant or facility layout design selection. International Journal of Industrial Engineering Computations, 3(3), 365–382. https://doi.org/10.5267/j.ijiec.2012.01.003
Roberts, D. W. (2017). Distance, dissimilarity, and mean–variance ratios in ordination. Methods in Ecology and Evolution, 8(11), 1398–1407. https://doi.org/10.1111/2041-210x.12739
Sachs, J. D., Lafortune, G., Fuller, G., & Iablonovski, G. (2025). Financing Sustainable Development to 2030 and Mid-Century. Sustainable Development Report 2025. Dublin University Press.
Sahoo, D., Parida, P. K., & Pati, B. (2024). Efficient fuzzy multi-criteria decision-making for optimal college location selection: A comparative study of min–max fuzzy TOPSIS approach. Results in Control and Optimization, 15, 100422. https://doi.org/10.1016/j.rico.2024.100422
Saltelli, A., Tarantola, S., Campolongo, F., & Ratto, M. (2000). Sensitivity analysis. John Wiley & Sons.
Sehgal, K., Kaur, H., Kaur, S., Singh, S., Channi, H. K., & Stević, Z. (2025). Cost-Effective Optimization of Hybrid Renewable Energy System for Micro, Small, and Medium Enterprises: A Decision-Making Framework Integrating MEREC and MARCOS. Opportunities and Challenges in Sustainability, 4(1), 17–32. https://doi.org/10.56578/ocs040102
Shafizadeh, E., & Hossein Mousavizadegan, S. (2025). A comprehensive risk assessment framework for composite Lenj Hulls: integrating FMEA with CRITIC-CODAS. Civil Engineering and Environmental Systems, 42(3–4), 314–334. https://doi.org/10.1080/10286608.2025.2548230
Shi, Y., Zhang, L., Peterson, C. B., Do, K.-A., & Jenq, R. R. (2022). Performance determinants of unsupervised clustering methods for microbiome data. Microbiome, 10(1). https://doi.org/10.1186/s40168-021-01199-3
Singh, R. (2025). COPRAS- and CODAS-based justification of factors affecting performance of manufacturing industries based in Northern India. International Journal of Process Management and Benchmarking, 19(4), 425–442. https://doi.org/10.1504/ijpmb.2025.144790
Soininen, J., Heino, J., & Wang, J. (2018). A meta-analysis of nestedness and turnover components of beta diversity across organisms and ecosystems. Global Ecology and Biogeography, 27(1), 96–109. https://doi.org/10.1111/geb.12660
Stević, Ž., Pamučar, D., Puška, A., & Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS). Computers & Industrial Engineering, 140, 106231. https://doi.org/10.1016/j.cie.2019.106231
Suriyanto, A. D., Akmaludin, A., & Widianto, K. (2025). MCDM-AHP and CODAS Collaboration Techniques for Selection of Expert Education Personnel. Sinkron, 9(2), 653–662. https://doi.org/10.33395/sinkron.v9i2.14182
Sutoyo, M. N. (2025). The Integration of DEMATEL and SAW Methods for Developing a Research Performance Assessment Model for Lecturers. Journal of Applied Data Sciences, 6(2), 1026–1036. https://doi.org/10.47738/jads.v6i2.550
Taherdoost, H. (2023). Analysis of Simple Additive Weighting Method (SAW) as a MultiAttribute Decision-Making Technique: A Step-by-Step Guide. Journal of Management Science & Engineering Research, 6(1), 21–24. https://doi.org/10.30564/jmser.v6i1.5400
Thakkar, J. J. (2021). Multi-Criteria Decision Making. Springer Singapore. https://doi.org/10.1007/978-981-33-4745-8
Thanh, N. V. (2020). Multi-Criteria Decision Making (MCDM) Model For Supplier Evaluation And Selection In The Supply Chain Management. Eliva Press.
Todeschini, R., Ballabio, D., Consonni, V., Mauri, A., & Pavan, M. (2007). CAIMAN (Classification And Influence Matrix Analysis): A new approach to the classification based on leverage-scaled functions. Chemometrics and Intelligent Laboratory Systems, 87(1), 3–17. https://doi.org/10.1016/j.chemolab.2005.11.001
Torkayesh, A. E., Deveci, M., Karagoz, S., & Antucheviciene, J. (2023). A state-of-the-art survey of evaluation based on distance from average solution (EDAS): Developments and applications. Expert Systems with Applications, 221, 119724. https://doi.org/10.1016/j.eswa.2023.119724
Towaiq, A., Obaidat, S., Mumani, A., & Ziout, A. (2025). An application of the preference selection index decision making method for parameters selection of FDM printing process. Journal of Engineering Research, 13(2), 636–642. https://doi.org/10.1016/j.jer.2023.11.010
Triantaphyllou, E. (2010). Multi-Criteria Decision Making Methods: A Comparative Study. Springer.
Turskis, Z., & Keršulienė, V. (2024). SHARDA–ARAS: A Methodology for Prioritising Project Managers in Sustainable Development. Mathematics, 12(2), 219. https://doi.org/10.3390/math12020219
Tuş, A., & Adalı, E. A. (2025). Enhancing Private Pension System Evaluation Using Fermatean Fuzzy Sets and Weighted Euclidean Distance Based Approach. Operations Research Forum, 6(3). https://doi.org/10.1007/s43069-025-00527-9
Wang, D., & Zhang, X. (2022). Application of the preference selection index method in multi-objective lightweight design of heavy commercial vehicle frames. Engineering Optimization, 55(6), 1020–1039. https://doi.org/10.1080/0305215x.2022.2058498
Wang, J., Cai, Q., Wei, G., & Liao, N. (2024). An Extended EDAS Approach Based on Cumulative Prospect Theory for Multiple Attributes Group Decision Making with Interval-Valued Intuitionistic Fuzzy Information. Informatica, 421–452. https://doi.org/10.15388/24-infor547
WIPO. (2025). Global Innovation Index 2025: Innovation at a Crossroads. World Intellectual Property Organization.
Yakowitz, D., Lane, L., & Szidarovszky, F. (1993). Multi-attribute decision making: dominance with respect to an importance order of the attributes. Applied Mathematics and Computation, 54(2–3), 167–181. https://doi.org/10.1016/0096-3003(93)90057-l
Yu, P., Ge, Z., Gong, X., & Cao, X. (2024). Dynamic portfolio optimization with the MARCOS approach under uncertainty. International Review of Financial Analysis, 96, 103565. https://doi.org/10.1016/j.irfa.2024.103565
Zardari, N. H., Ahmed, K., Shirazi, S. M., & Yusop, Z. B. (2015). Weighting Methods and their Effects on Multi-Criteria Decision Making Model Outcomes in Water Resources Management. Springer International Publishing. https://doi.org/10.1007/978-3-319-12586-2
Zavadskas, E. K., & Podvezko, V. (2016). Integrated Determination of Objective Criteria Weights in MCDM. International Journal of Information Technology & Decision Making, 15(02), 267–283. https://doi.org/10.1142/s0219622016500036
Zavadskas, E. K., & Turskis, Z. (2010). A New Additive Ratio Assesment (ARAS) Method in Multicriteria Decision-Making. Technological and Economic Development of Economy, 16(2), 159–172. https://doi.org/10.3846/tede.2010.10
Zavadskas, E. K., Turskis, Z., Antucheviciene, J., & Zakarevicius, A. (2012). Optimization of Weighted Aggregated Sum Product Assessment. Elektronika Ir Elektrotechnika, 122(6), 3–6. https://doi.org/10.5755/j01.eee.122.6.1810
Zhang, S., & Esangbedo, M. O. (2025). 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, 13(1), 67. https://doi.org/10.3390/systems13010067

Details

Primary Language

English

Subjects

Quantitative Decision Methods

Journal Section

Research Article

Authors

Furkan Fahri Altıntaş ^*
0000-0002-0161-5862
Türkiye

Publication Date

July 1, 2026

Submission Date

October 28, 2025

Acceptance Date

January 6, 2026

Published in Issue

Year 2026 Volume: 14 Number: 1

DOI

https://doi.org/10.17093/alphanumeric.1812325

IZ

https://izlik.org/JA77BC48AD

Cite

RIS / Bibtex

APA

Altıntaş, F. F. (2026). A scale independent and distribution sensitive approach in multi-criteria decision-making (MCDM): The Canberra distance performance measurement (CADPM) method. Alphanumeric Journal, 14(1), 65-112. https://doi.org/10.17093/alphanumeric.1812325

AMA

1.Altıntaş FF. A scale independent and distribution sensitive approach in multi-criteria decision-making (MCDM): The Canberra distance performance measurement (CADPM) method. Alphanumeric. 2026;14(1):65-112. doi:10.17093/alphanumeric.1812325

Chicago

Altıntaş, Furkan Fahri. 2026. “A Scale Independent and Distribution Sensitive Approach in Multi-Criteria Decision-Making (MCDM): The Canberra Distance Performance Measurement (CADPM) Method”. Alphanumeric Journal 14 (1): 65-112. https://doi.org/10.17093/alphanumeric.1812325.

EndNote

Altıntaş FF (July 1, 2026) A scale independent and distribution sensitive approach in multi-criteria decision-making (MCDM): The Canberra distance performance measurement (CADPM) method. Alphanumeric Journal 14 1 65–112.

IEEE

[1]F. F. Altıntaş, “A scale independent and distribution sensitive approach in multi-criteria decision-making (MCDM): The Canberra distance performance measurement (CADPM) method”, Alphanumeric, vol. 14, no. 1, pp. 65–112, July 2026, doi: 10.17093/alphanumeric.1812325.

ISNAD

Altıntaş, Furkan Fahri. “A Scale Independent and Distribution Sensitive Approach in Multi-Criteria Decision-Making (MCDM): The Canberra Distance Performance Measurement (CADPM) Method”. Alphanumeric Journal 14/1 (July 1, 2026): 65-112. https://doi.org/10.17093/alphanumeric.1812325.

JAMA

1.Altıntaş FF. A scale independent and distribution sensitive approach in multi-criteria decision-making (MCDM): The Canberra distance performance measurement (CADPM) method. Alphanumeric. 2026;14:65–112.

MLA

Altıntaş, Furkan Fahri. “A Scale Independent and Distribution Sensitive Approach in Multi-Criteria Decision-Making (MCDM): The Canberra Distance Performance Measurement (CADPM) Method”. Alphanumeric Journal, vol. 14, no. 1, July 2026, pp. 65-112, doi:10.17093/alphanumeric.1812325.

Vancouver

1.Furkan Fahri Altıntaş. A scale independent and distribution sensitive approach in multi-criteria decision-making (MCDM): The Canberra distance performance measurement (CADPM) method. Alphanumeric. 2026 Jul. 1;14(1):65-112. doi:10.17093/alphanumeric.1812325