Open Access

ARTICLE

Innovative Aczel Alsina Group Overlap Functions for AI-Based Criminal Justice Policy Selection under Intuitionistic Fuzzy Set

Ikhtesham Ullah¹, Muhammad Sajjad Ali Khan², Fawad Hussain¹, Madad Khan³, Kamran^4,*, Ioan-Lucian Popa^5,6,*

1 Department of Mathematics, Abbottabad University of Science and Technology, Abbottabad, 22500, Pakistan
2 Department of Mathematics, Khushal Khan Khattak University, Karak, 27200, Pakistan
3 Department of Mathematics, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, 22060, Pakistan
4 Department of Mathematics, Islamia College Peshawar, Khyber Pakhtoonkhwa, Peshawar, 25120, Pakistan
5 Department of Computing, Mathematics and Electronics, “1 Decembrie 1918” University of Alba Iulia, Alba Iulia, 510009, Romania
6 Faculty of Mathematics and Computer Science, Transilvania University of Brasov, Iuliu Maniu Street 50, Brasov, 500091, Romania

* Corresponding Authors: Kamran. Email: ; Ioan-Lucian Popa. Email:

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

Computer Modeling in Engineering & Sciences 2025, 144(2), 2123-2164. https://doi.org/10.32604/cmes.2025.064832

Received 25 February 2025; Accepted 10 July 2025; Issue published 31 August 2025

Abstract

Multi-criteria decision-making (MCDM) is essential for handling complex decision problems under uncertainty, especially in fields such as criminal justice, healthcare, and environmental management. Traditional fuzzy MCDM techniques have failed to deal with problems where uncertainty or vagueness is involved. To address this issue, we propose a novel framework that integrates group and overlap functions with Aczel-Alsina (AA) operational laws in the intuitionistic fuzzy set (IFS) environment. Overlap functions capture the degree to which two inputs share common features and are used to find how closely two values or criteria match in uncertain environments, while the Group functions are used to combine different expert opinions into a single collective result. This study introduces four new aggregation operators: Group Overlap function-based intuitionistic fuzzy Aczel-Alsina (GOF-IFAA) Weighted Averaging (GOF-IFAAWA) operator, intuitionistic fuzzy Aczel-Alsina (GOF-IFAA) Weighted Geometric (GOF-IFAAWG), intuitionistic fuzzy Aczel-Alsina (GOF-IFAA) Ordered Weighted Averaging (GOF-IFAAOWA), and intuitionistic fuzzy Aczel-Alsina (GOF-IFAA) Ordered Weighted Geometric (GOF-IFAAOWG), which are rigorously defined and mathematically analyzed and offer improved flexibility in managing overlapping, uncertain, and hesitant information. The properties of these operators are discussed in detail. Further, the effectiveness, validity, activeness, and ability to capture the uncertain information, the developed operators are applied to the AI-based Criminal Justice Policy Selection problem. At last, the comparison analysis between prior and proposed studies has been displayed, and then followed by the conclusion of the result.

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Keywords

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