An investigation to offer conclusive recommendations on suitable benefit/cost criteria-based normalization methods for TOPSIS
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a popular multi-criteria decision-making method that ranks the available alternatives by examining the ideal-positive and ideal-negative solutions for each decision criterion. The first step of using TOPSIS is to normalize th...
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my.ums.eprints.382012024-02-09T03:13:24Z https://eprints.ums.edu.my/id/eprint/38201/ An investigation to offer conclusive recommendations on suitable benefit/cost criteria-based normalization methods for TOPSIS Anath Rau Krishnan Mohamad Rizal Hamid Geoffrey Harvey Tanakinjal Mohammad Fadhli Asli Bonaventure Boniface Mohd Fahmi Ghazali GB3-5030 Physical geography HD28-9999 Industries. Land use. Labor Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a popular multi-criteria decision-making method that ranks the available alternatives by examining the ideal-positive and ideal-negative solutions for each decision criterion. The first step of using TOPSIS is to normalize the presence of incommensurable data in the decision matrix. There are several normalization methods, and the choice of these methods does affect TOPSIS results. As such, some efforts were made in the past to compare and recommend suitable normalization methods for TOPSIS. However, such studies merely compared a limited collection of normalization methods or used a noncomprehensive procedure to evaluate each method’s suitability, leading to equivocal recommendations. This study, therefore, employed an alternate, comprehensive procedure to evaluate and recommend suitable benefit/cost criteria-based normalization methods for TOPSIS (out of ten methods extracted from past literature). The procedure was devised based on three evaluation metrics: the average Spearman’s rank correlation, average Pearson correlation, and standard deviation metrics, combined with the Borda count technique. • The first study examined the suitability of ten benefit/cost criteria-based normalization methods over TOPSIS. • Users should combine the sum-based method and vector method into the TOPSIS application for safer decision-making. • The maximum method (version I) or Jüttler’s-Körth’s method has an identical effect on TOPSIS results. Elsevier 2023 Article NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/38201/1/ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/38201/2/FULL%20TEXT.pdf Anath Rau Krishnan and Mohamad Rizal Hamid and Geoffrey Harvey Tanakinjal and Mohammad Fadhli Asli and Bonaventure Boniface and Mohd Fahmi Ghazali (2023) An investigation to offer conclusive recommendations on suitable benefit/cost criteria-based normalization methods for TOPSIS. MethodsX, 10. pp. 1-27. https://doi.org/10.1016/j.mex.2023.102227 |
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GB3-5030 Physical geography HD28-9999 Industries. Land use. Labor Anath Rau Krishnan Mohamad Rizal Hamid Geoffrey Harvey Tanakinjal Mohammad Fadhli Asli Bonaventure Boniface Mohd Fahmi Ghazali An investigation to offer conclusive recommendations on suitable benefit/cost criteria-based normalization methods for TOPSIS |
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Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a popular multi-criteria decision-making method that ranks the available alternatives by examining the ideal-positive and ideal-negative solutions for each decision criterion. The first step of using TOPSIS is to normalize the presence of incommensurable data in the decision matrix. There are several normalization methods, and the choice of these methods does affect TOPSIS results. As such, some efforts were made in the past to compare and recommend suitable normalization methods for TOPSIS. However, such studies merely compared a limited collection of normalization methods or used a noncomprehensive procedure to evaluate each method’s suitability, leading to equivocal recommendations. This study, therefore, employed an alternate, comprehensive procedure to evaluate and recommend suitable benefit/cost criteria-based normalization methods for TOPSIS (out of ten methods extracted from past literature). The procedure was devised based on three evaluation metrics: the average Spearman’s rank correlation, average Pearson correlation, and standard deviation metrics, combined with the Borda count technique. • The first study examined the suitability of ten benefit/cost criteria-based normalization methods over TOPSIS. • Users should combine the sum-based method and vector method into the TOPSIS application for safer decision-making. • The maximum method (version I) or Jüttler’s-Körth’s method has an identical effect on TOPSIS results. |
format |
Article |
author |
Anath Rau Krishnan Mohamad Rizal Hamid Geoffrey Harvey Tanakinjal Mohammad Fadhli Asli Bonaventure Boniface Mohd Fahmi Ghazali |
author_facet |
Anath Rau Krishnan Mohamad Rizal Hamid Geoffrey Harvey Tanakinjal Mohammad Fadhli Asli Bonaventure Boniface Mohd Fahmi Ghazali |
author_sort |
Anath Rau Krishnan |
title |
An investigation to offer conclusive recommendations on suitable benefit/cost criteria-based normalization methods for TOPSIS |
title_short |
An investigation to offer conclusive recommendations on suitable benefit/cost criteria-based normalization methods for TOPSIS |
title_full |
An investigation to offer conclusive recommendations on suitable benefit/cost criteria-based normalization methods for TOPSIS |
title_fullStr |
An investigation to offer conclusive recommendations on suitable benefit/cost criteria-based normalization methods for TOPSIS |
title_full_unstemmed |
An investigation to offer conclusive recommendations on suitable benefit/cost criteria-based normalization methods for TOPSIS |
title_sort |
investigation to offer conclusive recommendations on suitable benefit/cost criteria-based normalization methods for topsis |
publisher |
Elsevier |
publishDate |
2023 |
url |
https://eprints.ums.edu.my/id/eprint/38201/1/ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/38201/2/FULL%20TEXT.pdf https://eprints.ums.edu.my/id/eprint/38201/ https://doi.org/10.1016/j.mex.2023.102227 |
_version_ |
1792152910287601664 |