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Vol.1, No.2, 2022: pp.46-56
COMPARISON R AND CURLI METHODS FOR MULTI-CRITERIA DECISION MAKING
Authors:
Do Duc Trung
Received: 12.05.2022.
Accepted: 27.06.2022.
Available: 30.06.2022.
Abstract:
When multi-criteria decision making, decision makers will expend significant effort in selecting a data normalization method and a weighting method. If a mistake is made in those choices, it will result in decisions that do not find the best solution. Furthermore, with qualitative criteria, it is impossible to standardize the data. Similarly, determining the weights of criteria will be difficult if the criteria are in qualitative form. R and CURLI are two multi-criteria decision-making methods that do not require data normalization or the use of additional weighting methods for the criteria. They are therefore well suited for ranking alternatives when the criteria are both quantitative and qualitative. This study compares the two methods through three examples from different fields. The results show that these two methods jointly determine the best solution in all three fields and are also suitable when using other decision – making methods that require data normalization and have high requirements using the method of determining the weights for the criteria.
Keywords:
MCDM, R method, CURLI method, data normalization, weight
References:
[1] W. Brauers, Optimization methods for a stakeholder society. A revolution in economic thinking by multi-objective optimization. Springer, New York, 2004. https://doi.org/10.1007/978-1-4419-9178-2
[2] E. Triantaphyllou, Multi-criteria Decision Making Methods: A Comparative Study. Springer, New York, 2020. https://doi.org/10.1007/978-1-4757-3157-6
[3] S. Kusumadewi, S. Hartati, A. Harjoko, R. Wardoyo, Fuzzy Multi-Attribute Decision Making (FUZZY MADM). Yogyakarta: Penerbit Graha Ilmu, 2006.
[4] E.K. Zavadskas, Z. Turskis, J. Antucheviciene, A. Zakarevicius, Optimization of weighted aggregated sum product assessment. Elektron ir elektrotechnika, 122(6), 2012: 3-6. https://doi.org/10.5755/j01.eee.122.6.1810
[5] S. Mufazzal, S.M. Muzakkir, A New MultiCriterion Decision Making (MCDM) Method Based on Proximity Indexed Value for Minimizing Rank Reversals. Computers & Industrial Engineering, 119, 2018: 427-438.
https://doi.org/10.1016/j.cie.2018.03.045–
[6] C.L. Hwang, Y.J. Lai, T.Y. Liu, A new approach for multiple objective decision making. Computers & Operations Research, 20(8), 1993: 889-899. https://doi.org/10.1016/0305-0548(93)90109-V
[7] S. Opricovic, G.H. Tzeng, Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 2004: 445-455. https://doi.org/10.1016/S0377-2217(03)00020-1
[8] D. Pamucar, G. Cirovic, The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC). Expert Systems with Applications, 42, 2015:3016-3028.
https://dx.doi.org/10.1016/j.eswa.2014.11.057
[9] D.S. Pamuca, S.P. Tarle, T. Parezanovi, New hybrid multi-criteria decision-making DEMATEL -MAIRCA model: sustainable selection of a location for the development of multimodal logistics centre. Economic Research-Ekonomska Istrazivanja, 31(1), 2018: 1641-1665. https://doi.org/10.1080/1331677X.2018.1506706
[10] Z. Stevic, D. Pamucar, A. Puska, P. Chatterjee, Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement Alternatives and Ranking according to COmpromise Solution (MARCOS), Computers & Industrial Engineering, 140, 2020:1-33. https://doi.org/10.1016/j.cie.2019.106231
[11] M.K. Ghorabaee, E.K. Zavadskas, M. Amiri, J. Antucheviciene, Evaluation by an Area-based Method of Ranking Interval Type-2 Fuzzy Sets (EAMRIT-2F) for Multi-criteria Group Decisionmaking. Transformations in Business & Economics, 15(3), 2016: 76-95.
[12] 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.
[13] M. Zizovic, D. Pamucar, M. Albijanic, P. Chatterjee, I. Pribicevic, Eliminating Rank Reversal Problem Using a New Multi-Attribute Model-The RAFSI Method. Mathematics, 8(6), 2020: 1-16. https://doi.org/10.3390/math8061015
[14] M. Yazdani, P. Zarate, E.K. Zavadskas, Zenonas Turskis, A Combined Compromise Solution (CoCoSo) method for multi-criteria decisionmaking problems. Management Decision, 57(9), 2019: 2501-2519. https://doi.org/10.1108/MD-05-2017-0458
[15] M.K. Ghorabaee, E.K. Zavadskas, Z. Turskis, J. Antuchevicience, A new combinative distancebased assessment (CODAS) method for multicriteria decision-making. Economic Computation and Economic Cybernetics Studies and Research, 50(3), 2016: 25-44.
[16] K. Maniya, M.G. Bhatt, A selection of material using a novel type decision-making method: Preference selection index method. Materials and Design, 31, 2010: 1785-1789. https://doi.org/10.1016/j.matdes.2009.11.020
[17] S.R. Besharati, V. Dabbagh, H. Amini, A.A.D. Sarhan, J. Akbari, M. Hamdi, Z.C. Ong, Multiobjective selection and structural optimization of the gantry in a gantry machine tool for improving static, dynamic, and weight and cost performance. Concurrent Engineering: Research and Applications, 24, 2016: 83-93.
https://doi.org/10.1177/1063293X15597047
[18] R.V. Rao, J. Lakshmi, R-method: A simple ranking method for multi-attribute decisionmaking in the industrial environment. Journal of Project Management, 6, 2021: 223–230. https://doi.org/10.5267/j.jpm.2021.5.001
[19] R.K. James, J.A. David, A new method for group decision making and its application in medical trainee selection. Medical Education, 50, 2016: 1045-1053. https://doi.org/10.1111/medu.13112
[20] D.D. Trung, Multi-criteria decision making of turning operation based on PEG, PSI and CURLI methods. Manufacturing review, 9(9), 2022: 1-12. https://doi.org/10.1051/mfreview/202200
[21] Z. Wen, H. Liao, E.K. Zavadskas, MACONT: Mixed Aggregation by Comprehensive Normalization Technique for Multi-Criteria Analysis. Informatica, 31(4), 2020: 857–880. https://doi.org/10.15388/20-INFOR417
[22] 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.
https://doi.org/10.1504/IJIDS.2018.090667
[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. https://doi.org/10.35378/gujs.767525
[24] A. Aytekin, Comparative Analysis of the Normalization Techniques in the Context of MCDM Problems. Decision Making: Applications in Management and Engineering, 4(2), 2021: 1-27. https://doi.org/10.31181/dmame210402001a
[25] P. Chatterjee, S. Chakraborty, Investigating the effect of normalization norms in flexible manufacturing system selection using multicriteria decision-making methods. International Journal of Information and Decision Sciences, 7(3), 2014: 141-150.
[26] E. Mokotoff, E.G.J. Perez, Normalization procedures on multicriteria decision making – An Example on Environmental Problems. The 12 th International Conference on Enterprise Information Systems – Artificial Intelligence and Decision Support Systems, 2010: 206-211.
[27] D.D. Trung, Multi-criteria decision making under the MARCOS method and the weighting methods: applied to milling, grinding and turning processes. Manufacturing Review, 9(3), 2022: 1-13. https://doi.org/10.1051/mfreview/2022003
[28] D.D. Trung, Application of TOPSIS an PIV Methods for Multi – Criteria Decision Making in Hard Turning Process. Journal of Machine Engineering, 21(4), 2021: 57-71, 2021. https://doi.org/10.36897/jme/142599
[29] M.K. Ghorabaee, E.K. Zzavadskas, Z. Turskis, J. Antucheviciene, A new combinative distancebased assessment (CODAS) method for multicriteria decision-making. Economic Computation and Economic Cybernetics Studies and Research, 50(3), 2016: 25-44.
[30] D.D. Trung, A combination method for multicriteria decision making problem in turning process. Manufacturing review, 8(26), 2021: 1-17. https://doi.org/10.1051/mfreview/2021024
[31] D. Bozanic, D. Tesic, J. Kocic, Multi-criteria FUCOM – Fuzzy MABAC model for the selection of location for construction of single-span bailey bridge. Decision Making: Applications in Management and Engineering, 2(1), 2019: 132-146. https://doi.org/10.31181/dmame1901132b
© 2022 by the author. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)
Volume 3
Number 2
June 2024.
How to Cite
D.D. Trung, Comparison R and Curli Methods for Multi-Criteria Decision Making. Advanced Engineering Letters, 1(2), 2022: 46–56.
https://doi.org/10.46793/adeletters.2022.1.2.3
More Citation Formats
Trung, D. D. (2022). Comparison R and Curli Methods for Multi-Criteria Decision Making. Advanced Engineering Letters, 1(2), 46–56. https://doi.org/10.46793/adeletters.2022.1.2.3
Trung, Do Duc. “Comparison R and Curli Methods for Multi-Criteria Decision Making.” Advanced Engineering Letters, vol. 1, no. 2, 2022, pp. 46–56, https://doi.org/10.46793/adeletters.2022.1.2.3.
Trung, Do Duc. 2022. “Comparison R and Curli Methods for Multi-Criteria Decision Making.” Advanced Engineering Letters 1 (2): 46–56. https://doi.org/10.46793/adeletters.2022.1.2.3.
Trung, D.D. (2022).Comparison R and Curli Methods for Multi-Criteria Decision Making. Advanced Engineering Letters, 1(2), pp.46–56. doi: 10.46793/adeletters.2022.1.2.3.