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Le Dang Ha1

1Center for Mechanical Engineering, Hanoi University of Industry, Hanoi City, 100000, Vietnam

Received: 9 January 2023
Revised: 15 March 2023
Accepted: 24 March 2023
Published: 31 March 2023


Compromise Ranking of Alternatives from Distance to Ideal Solution (CRADIS) is a new MCDM method (discovered in 2022). It is built on a combination of three well-known methods, including Additive Ratio Assessment (ARAS), Measurement Alternatives and Ranking according to Compromise Solution (MARCOS), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). This method has the advantage of being resistant to the rank inversion phenomenon. However, if only the available data normalization (DN) method in this method is used, this method will only be usable in some cases. This study investigated the suitability of twelve data normalization methods combined with the CRADIS method. The solutions in four cases of four different fields were ranked using these twelve combination methods. Using these methods, the ranked results were compared with those of other MCDM methods. Four DN methods were appropriate in combination with the CRADIS method. The application scope of CRADIS method can be extended when using this DN method.


MCDM, CRADIS method, Data normalization, Suitable DNM, Weight method


[1] H. D. Arora, A. Naithani, Significance of TOPSIS approach to MADM in computing exponential divergence measures for pythagorean fuzzy sets. Decision Making: Applications in Management and Engineering, 5(1), 2022: 246- 263.
[2] M. Tutak, J. Brodny, Evaluating differences in the level of working conditions between the European union member states using TOPSIS and K-MEANS methods. Decision Making: Applications in Management and Engineering, 5(2), 2022: 1-19.
[3] A. Puška, Z. Stević, D. Pamučar, Evaluation and selection of healthcare waste incinerators using extended sustainability criteria and multi-criteria analysis methods. Environment, Development and Sustainability, 24, 2022: 11195–11225.
[4] A. Puška, M. Nedeljković, Z. Sarkocević, Z. Golubović, V. Ristić, I. Stojanović, Evaluation of Agricultural Machinery Using Multi-Criteria Analysis Methods. Sustainability, 14(14) 2022: 8675.
[5] V. Starčevic, V.Petrović, I. Mirović, L.Z. Tanasić, Z. Stević, J.Đ. Todorović, A Novel Integrated PCA‐DEA‐IMF SWARA‐CRADIS Model for Evaluating the Impact of FDI on the Sustainability of the Economic System. Sustainability, 14(20), 2022: 13587
[6] A. Aytekin, Energy, Environment, and Sustainability: A Multi-criteria Evaluation of Countries. Strategic Planning for Energy and the Environment, 41(3), 2022: 281–316.
[7] I. Stojanović, A. Puška, M. Selaković, A multi-criteria approach to the comparative analysis of the global innovation index on the example of the Western BALKAN countries. Economics, 10(2), 2022: 9-26.
[8] A. Puška, M. Nedeljković, R. Prodanović, R. Vladisavljević, R. Suzić, Market Assessment of Pear Varieties in Serbia Using Fuzzy CRADIS and CRITIC Methods. Agriculture, 12(2), 2022: 139.
[9] A. Puška, D. Bozanić, M. Nedeljković, M. Janošević, Green Supplier Selection in an Uncertain Environment in Agriculture Using a Hybrid MCDM Model: Z-Numbers–Fuzzy LMAW–Fuzzy CRADIS Model. Axioms, 11(9), 2022: 427.
[10] N. Vafaei, R.A. Ribeiro, L.M. Camarinha-Matos, Selecting Normalization Techniques for the Analytical Hierarchy Process. Technological Innovation for Life Improvement, 577, 2020: 43-52.
[11] W.C. Yang, S.H. Chon, C.M. Choe, J.Y. Yang, Materials selection method using TOPSIS with some popular normalization methods. Engineering Research Express, 3, 2021: 015020.
[12] S.H. Zolfan, M. Yazdani, D. Pamucar, P. Zarate, A VIKOR and TOPSIS focused reanalysis of the MADM methods based on logarithmic normalization. Facta universitatis, Series: Mechanical Engineering, 18(3), 2020: 341–355.
[13] H. Lai, H. Liao, Z. Wen, E.K. Zavadskas, A. Al- Barakati, An Improved CoCoSo Method with a Maximum Variance Optimization Model for Cloud Service Provider Selection. Inzinerine Ekonomika-Engineering Economics, 31(4), 2020: 411-424.
[14] N. Vafaei, Data Normalization in Decision Making Processes, Thesis of MSc in Defense Technologies. Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2020.
[15] A. Aytekin, comparative analysis of normalization techniques in the context of MCDM problems. Decision Making: Applications in Management and Engineering, 4(2), 2021: 1-25.
[16] R. Sarraf, M. P. McGuire, Effect of Normalization on TOPSIS and Fuzzy TOPSIS. Proceedings of the Conference on Information Systems Applied Research, 14, 2021: 5551.
[17] E. Budiman, U. Hairah, M.W. Haviluddin, Sensitivity Analysis of Data Normalization Techniques in Social Assistance Program Decision Making for Online Learning. Advances in Science, Technology and Engineering Systems Journal, 6(1), 2021: 49-56.
[18] S. Biswas, D. Pamučar, Combinative distance based assessment (CODAS) framework using logarithmic normalization for multi-criteria decision making. Serbian Journal of Management, 16(2), 2021: 321-340.
[19] S.T. Mhlanga, M. Lal, Influence of Normalization Techniques on Multi-criteria Decision-making Methods. Journal of Physics: Conference Series, 2224, 2022: 012076.
[20] N. Vafaei, R. A. Ribeiro, L. M. Camarinha-Matos, Normalization techniques for multi-criteria decision making: Analytical Hierarchy Process case study. In Doctoral Conference on Computing, Electrical and Industrial Systems, Costa de Caparica, Portugal, 2016: 261-269.
[21] N. Vafaei, R. A. Ribeiro, L. M. Camarinha-Matos, Data normalisation techniques in decision making: case study with TOPSIS method. International Journal of Information and Decision Sciences, 10(1), 2018: 19-38.
[22] N. Vafaei, R.A. Ribeiro, L.M. Camarinha-Matos, Assessing Normalization Techniques for Simple Additive Weighting Method. Procedia Computer Science, 199, 2022: 1229-1236.
[23] N. Ersoy, Selecting the Best Normalization Technique for ROV Method: Towards a Real Life Application. Gazi University Journal of Science, 34(2), 2021: 592-609.
[24] D.D. Trung, Development of data normalization methods for multi-criteria decision making: applying for MARCOS method. Manufacturing Review, 9(22), 2022: 1-15.
[25] S.A.A. Alrababah, A.J. Atyeh, Effect of Normalization Techniques in VIKOR Approach for Mining Product Aspects in Customer Reviews. International Journal of Computer Science and Network Security, 19(12), 2019: 112-118.
[26] E.K. Zavadskas, D. Stanujkic, D. Karabasevic, Z. Turskis, Analysis of the Simple WISP Method results using different normalization procedures. Studies in Informatics and Control, 31(1), 2022: 5-12.
[27] D.D. Trung, expanding data normalization method to CODAS method for multi-criteria decision making. Applied Engineering Letters, 7(2), 2022: 54-66.
[28] H.T. Dung, D.D. Trung, N.V. Thien, Comparison of multi-criteria decision making methods using the same data standardization method. Strojnícky časopis – Journal of Mechanical Engineering, 72(2), 2022: 57-72.
[29] D.D. Trung, Application of FUCA method for multi-criteria decision making in mechanical machining. Operational Research in Engineering Sciences: Theory and Applications, 5(3), 2022: 131-152.
[30] M. Varatharajulu, M. Duraiselvam, M.B. Kumar, G. Jayaprakash, N. Baskar, Multi criteria decision making through TOPSIS and COPRAS on drilling parameters of magnesium AZ91. Journal of Magnesium and Alloys, 10(10), 2021: 2857-2874.
[31] C. Ardil, Aircraft Selection Process Using Preference Analysis for Reference Ideal Solution (PARIS). International Journal of Aerospace and Mechanical Engineering, 14(3), 2020: 80-90.
[32] Z. Wen, H. Liao, E.K. Zavadskas, MACONT: Mixed Aggregation by Comprehensive Normalization Technique for Multi-Criteria Analysis. Informatica, 31(4), 2020: 857-880.
[33] B. Ivanović, A. Saha, Z. Stević, A. Puška, E.K. Zavadskas, Selection of truck mixer concrete pump using novel MEREC DNMARCOS model. Archives of Civil and Mechanical Engineering, 22, 2022: 173.
[34] M. Žižović, D. Pamučar, M. Albijanić, P. Chatterje, I. Pribićević, Eliminating Rank Reversal Problem Using a New Multi-Attribute Model-The RAFSI Method. Mathematics, 8(6), 2020: 1015.
[35] A. Tus, E. Aytaç Adalı, Personnel Assessment with CODAS and PSI Methods. The Journal of Operations Research, Statistics, Econometrics and Management Information Systems, 6(2), 2018: 243-256.
[36] M. Keshavarz Ghorabaee, E.K. Zavadskas, Z. Turskis, J. Antuchevicien, A new combinative distance-based assessment (CODAS) method for multi-criteria decision-making. Economic Computation and Economic Cybernetics Studies and Research, 50(3), 2016: 25-44.
[37] H.-A. Le, X.-T. Hoang, Q.-H. Trieu, D.-L. Pham, X.-H. Le, Determining the best dressing parameters for external cylindrical grinding using MABAC method. Applied Sciences, 12, 2020: 8287.
[38] Z. Bobar, D. Božanic, K. Djuric, D. Pamučar, Ranking and Assessment of the Efficiency of Social Media using the Fuzzy AHP-Z Number Model – Fuzzy MABAC. Acta Polytechnica Hungarica, 17(3), 2020: 43-70.

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Volume 9
Number 1
March 2024

Last Edition

Volume 9
Number 1
March 2024

How to Cite

L.D. Ha, Selection of Suitable Data Normalization Method to Combine With the CRADIS Method for Making Multi-Criteria Decision. Applied Engineering Letters, 8(1), 2023: 24-35.

More Citation Formats

Ha, L. D. (2023). Selection of Suitable Data Normalization Method to Combine with the CRADIS Method for Making Multi-Criteria Decision. Applied Engineering Letters8(1), 24-35.

Ha, Le Dang. “Selection of Suitable Data Normalization Method to Combine with the CRADIS Method for Making Multi-Criteria Decision.” Applied Engineering Letters, vol. 8, no. 1, 2023, pp. 24-35,

Ha, Le Dang. 2023. “Selection of Suitable Data Normalization Method to Combine with the CRADIS Method for Making Multi-Criteria Decision.” Applied Engineering Letters  8 (1): 24-35.

Ha, L.D. (2023). Selection of Suitable Data Normalization Method to Combine with the CRADIS Method for Making Multi-Criteria Decision. Applied Engineering Letters, 8(1), pp.24-35. doi: 10.18485/aeletters.2023.8.1.4.