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EXPANDING DATA NORMALIZATION METHOD TO CODAS METHOD FOR MULTI-CRITERIA DECISION MAKING
Authors:
Do Duc Trung1
1Faculty of Mechanical Engineering, Hanoi University of Industry, Hanoi City, 100000, Vietnam
Received: 18.04.2022.
Accepted: 09.06.2022.
Available: 30.06.2022.
Abstract:
Data normalization is the conversion of quantities of different dimensions to the same dimensionless form, which is required in multicriteria decision making (MCDM). The choice of data normalization method has a direct influence on the decision-making results. This study presents the combination of CODAS (COmbinative Distance-based ASsessment) method with six different data normalization methods including Linear normalization, Max – Min linear normalization, Vector normalization, Sum linear normalization, Logarithmic normalization, Max linear normalization . These six combinations have been applied in turn in three different examples. The number of alternatives, the number of criteria, and the method of the weight calculation in these examples are also different. From the results it was reported that only the combination of CODAS and Logarithmic normalization was not suitable. The combination of CODAS with some other data normalization methods not mentioned in this study and it needs to be done in the near future. This task was covered in the last part of this paper.
Keywords:
MCDM, CODAS method, Data normalization, Linear normalization, Max – Min linear normalization, Vector normalization
References:
[1] C. Zopounidis, M. Doumpos, Multiple Criteria Decision Making – Applications in Management and Engineering. Springer, 2017. https://doi.org/10.1007/978-3-319-39292-9
[2] 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
[3] D.D. Trung, Application of TOPSIS and PIV methods for multi-criteria decision making in hard turning process. Journal of Machine Engineering, 21(4), 2021: 57-71. https://doi.org/10.36897/jme/142599
[4] M. Keshavarz Ghorabaee, E.K. Zavadskas, Z. Turskis, J. Antucheviciene, 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.
[5] I.A. Badi, A. M. Abdulshahed, A.G. Shetwan, Supplier Selection Using Combinative Distance-Based Assessment (CODAS) Method for Multi-Criteria Decision-Making, Proceedings of the 1st International Conference on Management, Engineering and Environment (ICMNEE 2017), 28 september, 2017, Belgrade, Serbia, pp.395-407.
http://dx.doi.org/10.2139/ssrn.3177276
[6] F. Ruvalcaba, A. Azalia, P. Domingueze, Luis, G. Villalba, L.A. Almaraz Duran, Sara, A comparison between MOORA and CODAS methods under Pythagorean Fuzzy Sets. Revista de Innovación Sistemática, 3(10), 2019: 9-19. http://dx.doi.org/10.35429/JSI.2019.10.3.9.19
[7] N. Yalcin, N.Y. Pehlivan, Application of the Fuzzy CODAS Method Based on Fuzzy Envelopes for Hesitant Fuzzy Linguistic Term Sets: A Case Study on a Personnel Selection Problem. Symmetry, 11(4), 2019: 1-27. https://doi.org/10.3390/sym11040493
[8] M.K. Ghorabaee, M. Amiri, E.K. Zavadskas, R. Hooshmand, J. Antucheviciene, Fuzzy extension of the CODAS method for multicriteria market segment evaluation. Journal of Business Economics and Management, 18(1), 2017: 1-19. https://doi.org/10.3846/16111699.2016.1278559
[9] S. Karagoz, M. Deveci, V. Simic, N. Aydin, U. Bolukbas, A novel intuitionistic fuzzy MCDMbased CODAS approach for locating an authorized dismantling center: a case study of Istanbul. Waste Management & Research: The Journal for a Sustainable Circular Economy, 2020: 1-13. https://doi.org/10.1177/0734242X19899729
[10] E.A. Adali, A. Tus, Hospital site selection with distance-based multi-criteria decisionmaking methods. Internationla journal of healthcare management, 14(2), 2021: 534-544. https://doi.org/10.1080/20479700.2019.1674005
[11] I. Badi, A.M. Abdulshahed, A. Shetwan, A case study of supplier selection for a steel making company in Libya by using the combinative distance based assessment (CODAS) model, Decision Making: Applications in Management and Engineering, 1(1), 2018: 1- 12. https://doi.org/10.31181/dmame180101b
[12] M. Mathew, J. Thomas, Interval valued multi criteria decision making methods for the selection of flexible manufacturing system. International Journal of Data and Network Science, 3, 2019: 349–358.
https://doi.org/10.5267/j.ijdns.2019.4.001
[13] L. Chen, X. Gou, The application of probabilistic linguistic CODAS method based on new score function in multi-criteria decision-making. Computational and Applied Mathematics, 41(11), 2022: 1-25. https://doi.org/10.1007/s40314-021-01568-6
[14] S. Gul, A. Aydogdu, Novel Entropy Measure Definitions and Their Uses in a Modified Combinative Distance-Based Assessment (CODAS) Method Under Picture Fuzzy Environment. Informatica, 32(4), 2021: 759–794.
https://doi.org/10.15388/21-INFOR458
[15] K. Deveci, R. Cin, A. Kagızman, A modified interval valued intuitionistic fuzzy CODAS method and its application to multi-criteria selection among renewable energy alternatives in Turkey. Applied Soft Computing Journal, 96, 2020: 1-18. https://doi.org/10.1016/j.asoc.2020.106660
[16] A.I. Maghsoodi, H. Rasoulipanah, L.M. Lopez, H. Liao, E.K. Zavadskas, Integrating Intervalvalued Multi-granular 2-tuple Linguistic BWM-CODAS Approach with Target-based Attributes: Site Selection for a Construction Project. Computers & Industrial Engineering, 139, 2019: 1-30. https://doi.org/10.1016/j.cie.2019.106147
[17] E.K. Zavadskas, J. Antucheviciene, P. Chatterjee, Multiple-Criteria DecisionMaking (MCDM) Techniques for Business Processes Information Management. MPDI, 2019. https://doi.org/10.3390/books978-3-03897-643-1
[18] A. Jahan, K. L. Edwards, A state-of-the-art survey on the influence of normalization techniques in ranking: Improving the materials selection process in engineering design. Materials & Design, 65, 2015: 335–342.
https://doi.org/10.1016/j.matdes.2014.09.022
[19] 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
[20] 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
[21] N. Vafaei, R.A. Ribeiro, L.M. CamarinhaMatos, 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
[22] 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
[23] 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.
[24] E. Mokotoff, E. G. J. Perez, Normalization procedures on multicriteria decision making – An Example on Environmental Problems. The 12th International Conference on Enterprise Information Systems – Artificial Intelligence and Decision Support Systems (ICEIS 2010), Madeira, Portugal, 2010, pp. 206-211. https://doi.org/10.5220/0002896102060211
[25] N. Vafaei, R.A. Ribeiro, L.M. CamarinhaMatos, Normalization Techniques for MultiCriteria Decision Making: Analytical Hierarchy Process Case Study, Doctoral Conference on Computing, Electrical and Industrial Systems, Costa de Caparica, Portugal, 2017: 261-269. https://doi.org/10.1007/978-3-319-31165-4_26
[26] K. Palczewski, W. Sałabun, Influence of various normalization methods in PROMETHEE II: an empirical study on the selection of the airport location. Procedia Computer Science, 159, 2019: 2051-2060.
https://doi.org/10.1016/j.procs.2019.09.378
[27] T.M. Lakshmi, V.P. Venkatesan, A Comparison of Various Normalization in Techniques for Order Performance by Similarity to Ideal Solution (TOPSIS). International Journal of Computing Algorithm, 3(3), 2014: 255-259.
[28] R. Sarraf, M.P. McGuire, Effect of Normalization on TOPSIS and Fuzzy TOPSIS. 2021 Proceedings of the Conference on Information Systems Applied Research, Washington DC, 2021, pp.1-11.
[29] D.D. Trung, H.X. Thinh, A multi-criteria decision-making in turning process using the MAIRCA, EAMR, MARCOS and TOPSIS methods: A comparative study. Advances in Production Engineering & Management, 16(4), 2021: 443-456. https://doi.org/10.14743/apem2021.4.412
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)
Volume 9
Number 3
September 2024
Last Edition
Volume 9
Number 3
September 2024
How to Cite
D.D. Trung, Expanding Data Normalization Method to CODAS Method for Multi-Criteria Decision Making. Applied Engineering Letters, 7(2), 2022: 54-66.
https://doi.org/10.18485/aeletters.2022.7.2.2
More Citation Formats
Trung, D.D. (2022). Expanding Data Normalization Method to CODAS Method for Multi-Criteria Decision Making. Applied Engineering Letters, 7(2), 54-66. https://doi.org/10.18485/aeletters.2022.7.2.2
Trung, Do Duc. “Expanding Data Normalization Method to CODAS Method for Multi-Criteria Decision Making.” Applied Engineering Letters, vol. 7, no. 2, 2022, pp. 54-66, https://doi.org/10.18485/aeletters.2022.7.2.2.
Trung, Do Duc, 2022. “Expanding Data Normalization Method to CODAS Method for Multi-Criteria Decision Making.” Applied Engineering Letters 7 (2): 54-66. https://doi.org/10.18485/aeletters.2022.7.2.2.
Trung, D.D. (2022). Expanding Data Normalization Method to CODAS Method for Multi-Criteria Decision Making. Applied Engineering Letters, 7(2), pp.54-66.
doi: 10.18485/aeletters.2022.7.2.2.
Journal Menu
Archive
EXPANDING DATA NORMALIZATION METHOD TO CODAS METHOD FOR MULTI-CRITERIA DECISION MAKING
Authors:
Do Duc Trung1
1Faculty of Mechanical Engineering, Hanoi University of Industry, Hanoi City, 100000, Vietnam
Received: 18.04.2022.
Accepted: 09.06.2022.
Available: 30.06.2022.
Abstract:
Data normalization is the conversion of quantities of different dimensions to the same dimensionless form, which is required in multicriteria decision making (MCDM). The choice of data normalization method has a direct influence on the decision-making results. This study presents the combination of CODAS (COmbinative Distance-based ASsessment) method with six different data normalization methods including Linear normalization, Max – Min linear normalization, Vector normalization, Sum linear normalization, Logarithmic normalization, Max linear normalization . These six combinations have been applied in turn in three different examples. The number of alternatives, the number of criteria, and the method of the weight calculation in these examples are also different. From the results it was reported that only the combination of CODAS and Logarithmic normalization was not suitable. The combination of CODAS with some other data normalization methods not mentioned in this study and it needs to be done in the near future. This task was covered in the last part of this paper.
Keywords:
MCDM, CODAS method, Data normalization, Linear normalization, Max – Min linear normalization, Vector normalization
References:
[1] C. Zopounidis, M. Doumpos, Multiple Criteria Decision Making – Applications in Management and Engineering. Springer, 2017. https://doi.org/10.1007/978-3-319-39292-9
[2] 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
[3] D.D. Trung, Application of TOPSIS and PIV methods for multi-criteria decision making in hard turning process. Journal of Machine Engineering, 21(4), 2021: 57-71. https://doi.org/10.36897/jme/142599
[4] M. Keshavarz Ghorabaee, E.K. Zavadskas, Z. Turskis, J. Antucheviciene, A new combinative distance-based assessment (CODAS) method for multi-criteria decision-D. D. Trung / Applied Engineering Letters Vol.7, No.2, 54-66 (2022)65 making, Economic Computation and Economic Cybernetics Studies and Research, 50(3), 2016: 25-44.
[5] I.A. Badi, A. M. Abdulshahed, A.G. Shetwan, Supplier Selection Using Combinative Distance-Based Assessment (CODAS) Method for Multi-Criteria Decision-Making, Proceedings of the 1st International Conference on Management, Engineering and Environment (ICMNEE 2017), 28 september, 2017, Belgrade, Serbia, pp.395- 407. http://dx.doi.org/10.2139/ssrn.3177276
[6] F. Ruvalcaba, A. Azalia, P. Domingueze, Luis, G. Villalba, L.A. Almaraz Duran, Sara, A comparison between MOORA and CODAS methods under Pythagorean Fuzzy Sets. Revista de Innovación Sistemática, 3(10), 2019: 9-19. http://dx.doi.org/10.35429/JSI.2019.10.3.9.19
[7] N. Yalcin, N.Y. Pehlivan, Application of the Fuzzy CODAS Method Based on Fuzzy Envelopes for Hesitant Fuzzy Linguistic Term Sets: A Case Study on a Personnel Selection Problem. Symmetry, 11(4), 2019: 1-27. https://doi.org/10.3390/sym11040493
[8] M.K. Ghorabaee, M. Amiri, E.K. Zavadskas, R. Hooshmand, J. Antucheviciene, Fuzzy extension of the CODAS method for multicriteria market segment evaluation. Journal of Business Economics and Management, 18(1), 2017: 1-19. https://doi.org/10.3846/16111699.2016.1278559
[9] S. Karagoz, M. Deveci, V. Simic, N. Aydin, U. Bolukbas, A novel intuitionistic fuzzy MCDMbased CODAS approach for locating an authorized dismantling center: a case study of Istanbul. Waste Management & Research: The Journal for a Sustainable Circular Economy, 2020: 1-13. https://doi.org/10.1177/0734242X19899729
[10] E.A. Adali, A. Tus, Hospital site selection with distance-based multi-criteria decisionmaking methods. Internationla journal of healthcare management, 14(2), 2021: 534-544. https://doi.org/10.1080/20479700.2019.1674005
[11] I. Badi, A.M. Abdulshahed, A. Shetwan, A case study of supplier selection for a steel making company in Libya by using the combinative distance based assessment (CODAS) model, Decision Making: Applications in Management and Engineering, 1(1), 2018: 1- 12. https://doi.org/10.31181/dmame180101b
[12] M. Mathew, J. Thomas, Interval valued multi criteria decision making methods for the selection of flexible manufacturing system. International Journal of Data and Network Science, 3, 2019: 349–358. https://doi.org/10.5267/j.ijdns.2019.4.001
[13] L. Chen, X. Gou, The application of probabilistic linguistic CODAS method based on new score function in multi-criteria decision-making. Computational and Applied Mathematics, 41(11), 2022: 1-25. https://doi.org/10.1007/s40314-021-01568-6
[14] S. Gul, A. Aydogdu, Novel Entropy Measure Definitions and Their Uses in a Modified Combinative Distance-Based Assessment (CODAS) Method Under Picture Fuzzy Environment. Informatica, 32(4), 2021: 759–794. https://doi.org/10.15388/21-INFOR458
[15] K. Deveci, R. Cin, A. Kagızman, A modified interval valued intuitionistic fuzzy CODAS method and its application to multi-criteria selection among renewable energy alternatives in Turkey. Applied Soft Computing Journal, 96, 2020: 1-18. https://doi.org/10.1016/j.asoc.2020.106660
[16] A.I. Maghsoodi, H. Rasoulipanah, L.M. Lopez, H. Liao, E.K. Zavadskas, Integrating Intervalvalued Multi-granular 2-tuple Linguistic BWM-CODAS Approach with Target-based Attributes: Site Selection for a Construction Project. Computers & Industrial Engineering, 139, 2019: 1-30. https://doi.org/10.1016/j.cie.2019.106147
[17] E.K. Zavadskas, J. Antucheviciene, P. Chatterjee, Multiple-Criteria DecisionMaking (MCDM) Techniques for Business Processes Information Management. MPDI, 2019. https://doi.org/10.3390/books978-3-03897-643-1
[18] A. Jahan, K. L. Edwards, A state-of-the-art survey on the influence of normalization techniques in ranking: Improving the materials selection process in engineering design. Materials & Design, 65, 2015: 335–342. https://doi.org/10.1016/j.matdes.2014.09.022
[19] 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
[20] 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
[21] N. Vafaei, R.A. Ribeiro, L.M. CamarinhaMatos, 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
[22] 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
[23] 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.
[24] E. Mokotoff, E. G. J. Perez, Normalization procedures on multicriteria decision making – An Example on Environmental Problems. The 12th International Conference on Enterprise Information Systems – Artificial Intelligence and Decision Support Systems (ICEIS 2010), Madeira, Portugal, 2010, pp. 206-211. https://doi.org/10.5220/0002896102060211
[25] N. Vafaei, R.A. Ribeiro, L.M. CamarinhaMatos, Normalization Techniques for MultiCriteria Decision Making: Analytical Hierarchy Process Case Study, Doctoral Conference on Computing, Electrical and Industrial Systems, Costa de Caparica, Portugal, 2017: 261-269. https://doi.org/10.1007/978-3-319-31165-4_26
[26] K. Palczewski, W. Sałabun, Influence of various normalization methods in PROMETHEE II: an empirical study on the selection of the airport location. Procedia Computer Science, 159, 2019: 2051-2060. https://doi.org/10.1016/j.procs.2019.09.378
[27] T.M. Lakshmi, V.P. Venkatesan, A Comparison of Various Normalization in Techniques for Order Performance by Similarity to Ideal Solution (TOPSIS). International Journal of Computing Algorithm, 3(3), 2014: 255-259.
[28] R. Sarraf, M.P. McGuire, Effect of Normalization on TOPSIS and Fuzzy TOPSIS. 2021 Proceedings of the Conference on Information Systems Applied Research, Washington DC, 2021, pp.1-11.
[29] D.D. Trung, H.X. Thinh, A multi-criteria decision-making in turning process using the MAIRCA, EAMR, MARCOS and TOPSIS methods: A comparative study. Advances in Production Engineering & Management, 16(4), 2021: 443-456. https://doi.org/10.14743/apem2021.4.412
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)
Volume 9
Number 3
September 2024
Last Edition
Volume 9
Number 3
September 2024