Selecting the Best Normalization Technique for ROV Method: Towards a Real Life Application

Yıl 2021, Cilt: 34 Sayı: 2, 592 - 609, 01.06.2021

Nazlı Ersoy

https://doi.org/10.35378/gujs.767525

Cited By: 13

Öz

Normalization is one of the stages that have an impact on the results of MCDM problems. Choosing the right normalization technique leads the decision maker to the right results. Accordingly, the purpose of this study is to determine the most appropriate normalization technique for the ROV method. In this study, a real case is analyzed, eight different normalization methods are compared with each other on the basis of a multi-stage framework. The findings show that the model used in this study can be successfully applied in the selection of normalization technique. This study provides a decision support and reference for the selection of nomalization technique for MCDM methods in terms of the framework used. Another importance of this study is the first testing the suitability of different normalization techniques for the ROV method.

Anahtar Kelimeler

MCDM, Range of Value (ROV) method, Normalization, Consistency, Performance evaluation

Kaynakça

• Jahan, A., Edwards, K.L., “A state-of-the-art survey on the influence of normalization techniques in ranking: improving the materials selection process in engineering design”, Materials & Design, 65:335–342, (2015).
• Vafaei, N., Ribeiro, R. A., Camarinha-Matos, L. M., “Selecting Normalization Techniques for the Analytical Hierarchy Process”, Doctoral Conference on Computing, Electrical and Industrial Systems, 43-52, (2020).
• Pavlicic, D., “Normalization affects the results of MADM methods”, Yugoslav Journal of Operations Research, 11(2):251–265, (2001).
• Milani, A.S., Shanian, R., Madoliat, R. and Nemes, J.A., “The effect of normalization norms in multiple attribute decision making models: a case study in gear material selection”, Structural and Multidisciplinary Optimization, 29(4):312–318, (2005).
• Zavadskas, E.K., Zakarevicius, A., Antucheviciene, J., “Evaluation of ranking accuracy in multi-criteria decisions”, Informatica, 17(4):601–617, (2006).
• Lai, Y. J., Hwang, C. L. “Fuzzy multiple objective decision making: Methods and Applications”. Berlin: Springer-Verlag, (1994).
• Brauers, W.K., Zavadskas, E.K., “The MOORA method and its application to privatization in a transition economy”, Control and Cybernetics, 35(2):443–468, (2006).
• Mathew, M., Sahu, S., Upadhyay, A. K., “Effect Of Normalization Techniques In Robot Selection Using Weighted Aggregated Sum Product Assessment,” Int. J. Innov. Res. Adv. Stud., 4(2):59-63, (2017).
• Chakraborty, S. and Yeh, C. H., “A simulation based comparative study of normalization procedures in multiattribute decision making”, 6th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases, Corfu Island, Greece, 102-109, (2007).
• Chakraborty, S.,Yeh, C-H., “A simulation comparison of normalization procedures for TOPSIS’, Computing Industrial Engineering, 5(9):1815–1820, (2009).
• Celen, A., “Comparative analysis of normalization procedures in TOPSIS method: with an application to Turkish deposit banking market”, Informatica, 25(2):185-208, (2014).
• Shannon, C.E., “A Mathematical Theory Of Communication”, Bell System Technical Journal, 27:379-423, (1948).
• Wu, Z., Sun, J., Liang, L., Zha, Y., “Determination of Weights For Ultimate Cross Efficiency Using Shannon Entropy”, Expert Systems With Applications, 38:5162–5165, (2011).
• Wang, T. C., Lee, H. D., “Developing a fuzzy TOPSIS approach based on subjective weights and objective weights”, Expert systems with applications, 36(5):8980-8985, (2009).
• Yakowitz, D. S., Lane, L. J., Szidarovszky, F., “Multi-attribute decision making: dominance with respect to an importance order of the attributes”, Applied Mathematics and Computation, 54(2-3):167-181, (1993).
• Madić, M., Radovanović, M., Manić, M., “Application of the ROV method for the selection of cutting fluids”, Decision Science Letters, 5(2):245-254, (2016).
• Madić, M., Radovanović, M., “Ranking Of Some Most Commonly Used Non-Traditional Machining Processes Using Rov And Critic Methods” Upb Sci. Bull., Series D, 77(2):193-204, (2015).
• Shanian, A. and Savadogo, O., “TOPSIS multiple-criteria decision support analysis for material selection of metallic bipolar plates for polymer electrolyte fuel cell”, Journal of Power Sources, 159(2):1095-1104, (2006).
• Delft, A. D., Nijkamp, P. “Multi-Criteria Analysis and Regional Decision-Making”. Springer Science & Business Media, Berlin, Almanya, (1977).
• Zavadskas, E. K., Turskis, Z., “A new logarithmic normalization method in games theory”, Informatica, 19(2):303-314, (2008).
• Jee, D. H., Kang, K. J., “A method for optimal material selection aided with decision making theory”, Materials & Design, 21(3):199-206, (2000).
• Wang, Y. M., Luo, Y., “Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making”, Mathematical and Computer Modelling, 51(1-2):1-12, (2010).
• Stanujkic, D., Dordevic, B., Dordevic, M., “Comparative analysis of some prominent MCDM Methods:A case of ranking Serbian banks”, Serbian Journal of Management, 8(2):213-241, (2013).
• Zeng, Q.L., Li, D.D., Yang ,Y. B., “VIKOR Method with enhanced Accuracy for multiple criterias decision making in healthcare management”, Journal of medical system, 37:1-9, (2013).
• Peldschus, F., Vaigauskas, E., Zavadskas, E. K., “Technologische Entscheidungen bei der Berücksichtigung mehrerer Ziehle”, Bauplanung Bautechnik, 37(4):173-175, (1983).
• Asgharpour, M. J. “Multiple criteria decision making”. Tehran University Press, Tehran, (1998).
• Tzeng, G. H. and Huang, J. J. “Multiple attribute decision making: methods and applications”. CRC press, Florida, ABD, (2011).
• Shih, H. S., Shyur, H. J., Lee, E. S., “An extension of TOPSIS for group decision making”, Mathematical and computer modelling, 45(7-8):801-813, (2007).
• Farag, M. M. “Materials selection for engineering design”. Prentice Hall, (1997).
• Zhou, P., Ang, B. W., Poh, K. L., “Comparing aggregating methods for constructing the composite environmental index: An objective measure”, Ecological Economics,59(3):305-311, (2006).
• Markovic, Z., “Modification of TOPSIS method for solving of multicriteria tasks”, The Yugoslav Journal of Operations Research, 20(1):117-143, (2010).
• Vafaei, N., Ribeiro, R. A., Camarinha-Matos, L. M., Valera, L. R. “Normalization techniques for collaborative Networks”, Kybernetes, 49(4):1285-1304, (2019).
• Jahan, A., “Developing WASPAS-RTB method for range target-based criteria: toward selection for robust design”, Technological and Economic Development of Economy, 24(4):1362–1387, (2018).
• Chatterjee, P., Chakraborty, S., “Investigating the effect of normalization norms in flexible manufacturing sytem selection using multi-criteria decision-making methods”, Journal of Engineering Science and Technology Review, 7(3):141 -150, (2014).
• Bland, J.M., Altman, D.G., “Statistics notes: Measurement error”, BMJ, 313(7059):744-744, (1996).
• Rumsey, D.J. “Statistics II for Dummies”. Wiley Publishing, New Jersey, ABD, (2009).
• Yeh, C. H., “The selection of multiattribute decision making methods for scholarship student selection”, International Journal of Selection and Assessment, 11(4):289-296, (2003).
• Guo, Q. “Minkowski Measure of Asymmetry and Minkowski Distance for Convex Bodies”. Matematiska institutionen, (2004).
• Hassan, D., Aickelin, U., Wagner, C., “Comparison of distance metrics for hierarchical data in medical databases”, 2014 International Joint Conference on Neural Networks (IJCNN). IEEE, 3636-3643, (2014).
• Vafaei, N., Ribeiro, R. A., Camarinha-Matos, L. M., “Data normalisation techniques in decision making: case study with TOPSIS method”, Int. J. Inf. Decis. Sci., 10(1):19-38, (2014).
• d’Angelo, A., Eskandari, A., Szidarovszky, F., “Social choice procedures in water resource management”, Journal of Environmental Management, 52(3):203–210, (1998).