Adaptive Cluster-Based Normalization for Robust TOPSIS in Multicriteria Decision-Making
In multicriteria decision-making (MCDM), methods such as TOPSIS are essential for evaluating and comparing alternatives across multiple criteria. However, traditional normalization techniques often struggle with datasets containing outliers, large variances, or heterogeneous measurement units, which...
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MDPI AG
2025-04-01
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| author | Vitor Anes António Abreu |
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| collection | DOAJ |
| description | In multicriteria decision-making (MCDM), methods such as TOPSIS are essential for evaluating and comparing alternatives across multiple criteria. However, traditional normalization techniques often struggle with datasets containing outliers, large variances, or heterogeneous measurement units, which can lead to skewed or biased rankings. To address these challenges, this paper proposes an adaptive, cluster-based normalization approach, demonstrated through a real-world logistics case study involving the selection of a host city for an international event. The method groups alternatives into clusters based on similarities in criterion values and applies logarithmic normalization within each cluster. This localized strategy reduces the influence of outliers and ensures that scaling adjustments reflect the specific characteristics of each group. In the case study—where cities were evaluated based on cost, infrastructure, safety, and accessibility—the cluster-based normalization method yielded more stable and balanced rankings, even in the presence of significant data variability. By reducing the influence of outliers through logarithmic normalization and allowing predefined cluster profiles to reflect expert judgment, the method improves fairness and adaptability. These features strengthen TOPSIS’s ability to deliver accurate, balanced, and context-aware decisions in complex, real-world scenarios. |
| format | Article |
| id | doaj-art-7b1fd8dbc21e4f5a8d666dc7e240f503 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-7b1fd8dbc21e4f5a8d666dc7e240f5032025-08-20T03:06:20ZengMDPI AGApplied Sciences2076-34172025-04-01157404410.3390/app15074044Adaptive Cluster-Based Normalization for Robust TOPSIS in Multicriteria Decision-MakingVitor Anes0António Abreu1IDMEC, Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, 1959-007 Lisbon, PortugalUnit for Innovation and Research in Engineering, Polytechnic University of Lisbon, 1959-007 Lisbon, PortugalIn multicriteria decision-making (MCDM), methods such as TOPSIS are essential for evaluating and comparing alternatives across multiple criteria. However, traditional normalization techniques often struggle with datasets containing outliers, large variances, or heterogeneous measurement units, which can lead to skewed or biased rankings. To address these challenges, this paper proposes an adaptive, cluster-based normalization approach, demonstrated through a real-world logistics case study involving the selection of a host city for an international event. The method groups alternatives into clusters based on similarities in criterion values and applies logarithmic normalization within each cluster. This localized strategy reduces the influence of outliers and ensures that scaling adjustments reflect the specific characteristics of each group. In the case study—where cities were evaluated based on cost, infrastructure, safety, and accessibility—the cluster-based normalization method yielded more stable and balanced rankings, even in the presence of significant data variability. By reducing the influence of outliers through logarithmic normalization and allowing predefined cluster profiles to reflect expert judgment, the method improves fairness and adaptability. These features strengthen TOPSIS’s ability to deliver accurate, balanced, and context-aware decisions in complex, real-world scenarios.https://www.mdpi.com/2076-3417/15/7/4044TOPSISlogarithmic normalizationcluster-based normalizationmulticriteria decision-makingoutlier mitigation |
| spellingShingle | Vitor Anes António Abreu Adaptive Cluster-Based Normalization for Robust TOPSIS in Multicriteria Decision-Making Applied Sciences TOPSIS logarithmic normalization cluster-based normalization multicriteria decision-making outlier mitigation |
| title | Adaptive Cluster-Based Normalization for Robust TOPSIS in Multicriteria Decision-Making |
| title_full | Adaptive Cluster-Based Normalization for Robust TOPSIS in Multicriteria Decision-Making |
| title_fullStr | Adaptive Cluster-Based Normalization for Robust TOPSIS in Multicriteria Decision-Making |
| title_full_unstemmed | Adaptive Cluster-Based Normalization for Robust TOPSIS in Multicriteria Decision-Making |
| title_short | Adaptive Cluster-Based Normalization for Robust TOPSIS in Multicriteria Decision-Making |
| title_sort | adaptive cluster based normalization for robust topsis in multicriteria decision making |
| topic | TOPSIS logarithmic normalization cluster-based normalization multicriteria decision-making outlier mitigation |
| url | https://www.mdpi.com/2076-3417/15/7/4044 |
| work_keys_str_mv | AT vitoranes adaptiveclusterbasednormalizationforrobusttopsisinmulticriteriadecisionmaking AT antonioabreu adaptiveclusterbasednormalizationforrobusttopsisinmulticriteriadecisionmaking |