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Journals / Gazi University Journal of Science / 2021 / Cilt: 34 - Sayı: 2

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

Nazlı ERSOY
Pages
592–609
Publication Date
2022-11-06
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Another importance of this study is the first testing the suitability of different normalization techniques for the ROV method.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Normalization is one of the stages that have an impact on the results of MCDM problems.
📊
Key Findings & Data:Choosing the right normalization technique leads the decision maker to the right results.
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Conclusion & Impact:Another importance of this study is the first testing the suitability of different normalization techniques for the ROV method.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

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.

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Cite This Article

ERSOY, N. (2022). Selecting the Best Normalization Technique for ROV Method: Towards a Real Life Application. Gazi University Journal of Science, Cilt: 34 - Sayı: 2, 592–609. https://ajindex.com/articles/650933

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References (13)

  1. Jahan, A. and 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)
  2. Jana, C., Senapati, T. and Pal, M., “Yager, R. R., Picture fuzzy Dombi aggregation operators: Application to MADM process”, Applied Soft Computing, 74, 99-109, (2019)
  3. Jana, C., Pal, M. and Wang, J. Q., “Bipolar fuzzy Dombi aggregation operators and its application in multiple-attribute decision-making process”, Journal of Ambient Intelligence and Humanized Computing, 10(9), 3533-3549, (2019)
  4. Jana, C., Pal, M., Karaaslan, F. and Wang, J. Q., “Trapezoidal neutrosophic aggregation operators and their application to the multi-attribute decision-making process”, Transaction on Industrial Engineering, 27(3), 1655-1673, (2020)
  5. Vafaei, N., Ribeiro, R. A. and Camarinha-Matos, L. M., “Selecting Normalization Techniques for the Analytical Hierarchy Process”, Doctoral Conference on Computing, Electrical and Industrial Systems, Lisbon, Portugal 43-52, (2020)
  6. Pavlicic, D., “Normalization affects the results of MADM methods”, Yugoslav Journal of Operations Research, 11(2): 251–265, (2001)
  7. 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)
  8. Zavadskas, E.K., Zakarevicius, A. and Antucheviciene, J., “Evaluation of ranking accuracy in multi- criteria decisions”, Informatica, 17(4): 601–617, (2006)
  9. Lai, Y. J. and Hwang, C. L., “Fuzzy multiple objective decision making: Methods and Applications”, Berlin: Springer-Verlag, (1994)
  10. Brauers, W.K. and Zavadskas, E.K., “The MOORA method and its application to privatization in a transition economy”, Control and Cybernetics, 35(2): 443–468, (2006)
  11. Mathew, M., Sahu, S. and 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)
  12. 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)
  13. Chakraborty, S. and Yeh, C-H., “A simulation comp