Author(s):
Anes, Vitor ; Abreu, António
Date: 2025
Persistent ID: http://hdl.handle.net/10362/183914
Origin: Repositório Institucional da UNL
Project/scholarship:
info:eu-repo/grantAgreement/FCT/Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017%2F2018) - Financiamento Base/UIDB%2F50022%2F2020/PT;
Subject(s): cluster-based normalization; logarithmic normalization; multicriteria decision-making; outlier mitigation; TOPSIS; Materials Science(all); Instrumentation; Engineering(all); Process Chemistry and Technology; Computer Science Applications; Fluid Flow and Transfer Processes
Description
Funding Information: The authors gratefully acknowledge the support from FCT–Fundação para a Ciência e Tecnologia (Portuguese Foundation for Science and Technology), through IDMEC, under LAETA Base Funding (DOI: 10.54499/UIDB/50022/2020). Publisher Copyright: © 2025 by the authors.
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.
