Open Access

ARTICLE

Dombi Power Aggregation-Based Decision Framework for Smart City Initiative Prioritization under t-Arbicular Fuzzy Environment

Jawad Ali^1,*, Ioan-Lucian Popa^2,3

1 Institute of Numerical Sciences, Kohat University of Science and Technology, Kohat, 26000, Khyber Pakhtunkhwa, Pakistan
2 Department of Computing, Mathematics and Electronics, “1 Decembrie 1918” University of Alba Iulia, Alba Iulia, 510009, Romania
3 Faculty of Mathematics and Computer Science, Transilvania University of Brasov, Iuliu Maniu Street 50, Brasov, 500091, Romania

* Corresponding Author: Jawad Ali. Email:

(This article belongs to the Special Issue: Algorithms, Models, and Applications of Fuzzy Optimization and Decision Making)

Computer Modeling in Engineering & Sciences 2025, 145(1), 857-889. https://doi.org/10.32604/cmes.2025.064604

Received 19 February 2025; Accepted 29 July 2025; Issue published 30 October 2025

Abstract

With the rapid growth of urbanization, smart city development has become a strategic priority worldwide, requiring complex and uncertain decision-making processes. In this context, advanced decision-support tools are essential to evaluate and prioritize competing initiatives effectively. To support effective prioritization of smart city initiatives under uncertainty, this study introduces a robust decision-making framework based on the t-arbicular fuzzy (t-AF) set—a recent extension of the t-spherical fuzzy set that incorporates an additional parameter, the radius , to enhance the representation of uncertainty. Dombi-based operational laws are formulated within this context, leading to the development of four power aggregation operators that integrate a support degree to reflect inter-attribute relationships. The structural and theoretical foundations of the operators are rigorously demonstrated. Further, the proposed operators are embedded into an extended weighted aggregated sum product assessment (WASPAS) method to create a comprehensive multi-criteria decision-making model. The practical utility of the proposed approach is demonstrated through a case study involving the evaluation of seven smart city initiatives against eight critical criteria. Comparative analysis against established models reveals that the proposed approach offers superior ranking consistency, enhanced discrimination power among alternatives, and improved handling of uncertainty—ultimately supporting more reliable and interpretable decision-making outcomes.

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Keywords

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