@Article{cmes.2025.068734,
AUTHOR = {Sijia Zhu, Yuhan Li, Prasanalakshmi Balaji, Akila Thiyagarajan, Rajanikanth Aluvalu, Zhe Liu},
TITLE = {An Improved Interval-Valued Picture Fuzzy TOPSIS Approach Based on New Divergence Measures for Risk Assessment},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {144},
YEAR = {2025},
NUMBER = {2},
PAGES = {2099--2121},
URL = {http://www.techscience.com/CMES/v144n2/63730},
ISSN = {1526-1506},
ABSTRACT = {While interval-valued picture fuzzy sets (IvPFSs) provide a powerful tool for modeling uncertainty and ambiguity in various fields, existing divergence measures for IvPFSs remain limited and often produce counterintuitive results. To address these shortcomings, this paper introduces two novel divergence measures for IvPFSs, inspired by the Jensen-Shannon divergence. The fundamental properties of the proposed measures—non-degeneracy, symmetry, triangular inequality, and boundedness—are rigorously proven. Comparative analyses with existing measures are conducted through specific cases and numerical examples, clearly demonstrating the advantages of our approach. Furthermore, we apply the new divergence measures to develop an enhanced interval-valued picture fuzzy TOPSIS method for risk assessment in construction projects, showing the practical applicability and effectiveness of our contributions.},
DOI = {10.32604/cmes.2025.068734}
}