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Past efforts in determining suitable normalization methods for multi-criteria decision-making: A short survey

The use of a multi-criteria decision-making (MCDM) technique mostly begins with normalizing the incommensurable data values in the decision matrix. Numerous normalization methods are available in the literature and applying different normalization methods to an MCDM technique is proven to deliver va...

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Main Author: Anath Rau Krishnan
Format: Article
Language:English
English
Published: Frontiers Media S.A. 2022
Subjects:
HF5001-6182 Business
Online Access:https://eprints.ums.edu.my/id/eprint/34692/1/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/34692/2/ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/34692/
https://www.frontiersin.org/articles/10.3389/fdata.2022.990699/full
https://doi.org/10.3389/fdata.2022.990699
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spelling my.ums.eprints.346922022-11-08T00:29:43Z https://eprints.ums.edu.my/id/eprint/34692/ Past efforts in determining suitable normalization methods for multi-criteria decision-making: A short survey Anath Rau Krishnan HF5001-6182 Business The use of a multi-criteria decision-making (MCDM) technique mostly begins with normalizing the incommensurable data values in the decision matrix. Numerous normalization methods are available in the literature and applying different normalization methods to an MCDM technique is proven to deliver varying results. As such, selecting suitable normalization methods for an MCDM technique has emerged as an intriguing research topic, especially with the advent of big data. Several efforts have been made to compare the suitability of various normalization methods, but regrettably, no paper provides an updated review of these crucial efforts. This study, therefore, aimed to trace articles reporting such efforts and review them based on the following three perspectives: (1) the normalization methods considered, (2) the MCDM methods considered, and (3) the comparison metrics used to determine the suitable normalization methods. The relevant articles were extracted with the aid of Google Scholar using the keywords of “normalization” and “MCDM,” and Tableau software was used to analyze further the data gathered through the articles. A total of five limitations were uncovered based on the current state of literature, and potential future works to address those limitations were offered. This paper is the first to compile and review the previous investigations that compared and determined the ideal normalization methods for an MCDM technique. Frontiers Media S.A. 2022 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/34692/1/FULL%20TEXT.pdf text en https://eprints.ums.edu.my/id/eprint/34692/2/ABSTRACT.pdf Anath Rau Krishnan (2022) Past efforts in determining suitable normalization methods for multi-criteria decision-making: A short survey. Frontiers in Big Data, 5 (990699). pp. 1-10. ISSN 2624-909X https://www.frontiersin.org/articles/10.3389/fdata.2022.990699/full https://doi.org/10.3389/fdata.2022.990699
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic HF5001-6182 Business
spellingShingle HF5001-6182 Business
Anath Rau Krishnan
Past efforts in determining suitable normalization methods for multi-criteria decision-making: A short survey
description The use of a multi-criteria decision-making (MCDM) technique mostly begins with normalizing the incommensurable data values in the decision matrix. Numerous normalization methods are available in the literature and applying different normalization methods to an MCDM technique is proven to deliver varying results. As such, selecting suitable normalization methods for an MCDM technique has emerged as an intriguing research topic, especially with the advent of big data. Several efforts have been made to compare the suitability of various normalization methods, but regrettably, no paper provides an updated review of these crucial efforts. This study, therefore, aimed to trace articles reporting such efforts and review them based on the following three perspectives: (1) the normalization methods considered, (2) the MCDM methods considered, and (3) the comparison metrics used to determine the suitable normalization methods. The relevant articles were extracted with the aid of Google Scholar using the keywords of “normalization” and “MCDM,” and Tableau software was used to analyze further the data gathered through the articles. A total of five limitations were uncovered based on the current state of literature, and potential future works to address those limitations were offered. This paper is the first to compile and review the previous investigations that compared and determined the ideal normalization methods for an MCDM technique.
format Article
author Anath Rau Krishnan
author_facet Anath Rau Krishnan
author_sort Anath Rau Krishnan
title Past efforts in determining suitable normalization methods for multi-criteria decision-making: A short survey
title_short Past efforts in determining suitable normalization methods for multi-criteria decision-making: A short survey
title_full Past efforts in determining suitable normalization methods for multi-criteria decision-making: A short survey
title_fullStr Past efforts in determining suitable normalization methods for multi-criteria decision-making: A short survey
title_full_unstemmed Past efforts in determining suitable normalization methods for multi-criteria decision-making: A short survey
title_sort past efforts in determining suitable normalization methods for multi-criteria decision-making: a short survey
publisher Frontiers Media S.A.
publishDate 2022
url https://eprints.ums.edu.my/id/eprint/34692/1/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/34692/2/ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/34692/
https://www.frontiersin.org/articles/10.3389/fdata.2022.990699/full
https://doi.org/10.3389/fdata.2022.990699
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score 13.211869

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