Linear Diophantine Fuzzy Aggregation Operators with Multi-Criteria Decision-Making

Muhammad Riaz; Hafiz Muhammad Athar Farid; Faruk Karaaslan

doi:10.47852/bonviewJCCE3202420

Authors

Muhammad Riaz Department of Mathematics, University of the Punjab, Pakistan https://orcid.org/0000-0001-8115-9168
Hafiz Muhammad Athar Farid Department of Mathematics, University of the Punjab, Pakistan
Faruk Karaaslan Department of Mathematics, Çankırı Karatekin University, Türkiye https://orcid.org/0000-0002-0836-6264

DOI:

https://doi.org/10.47852/bonviewJCCE3202420

Keywords:

aggregation operators, linear Diophantine fuzzy number, multi-criteria decision-making (MCDM)

Abstract

A linear Diophantine fuzzy set (LDFS) is a new mathematical tool that can be used for optimization, artificial intelligence, and process modeling. The LDFS theory widens the area of fuzzy information via “reference parameters” due to its wonderful characteristic of abroad depiction zone for allowed doublets. Because the actual world is not exact, and there is a dearth of knowledge, determining and selecting the optimal choice is a tough and unforeseen decision-making dilemma. The primary goal is to guide decision-makers through the process of selecting the best option inside a linear Diophantine fuzzy context. We suggested two new aggregation operators: the “linear Diophantine fuzzy weighted average operator and the linear Diophantine fuzzy weighted geometric operator.” Following that, the proposed model is validated using a clear example of linear Diophantine fuzzy content. This demonstrates the utility and applicability of the suggested strategy model is validated using a clear example of linear Diophantine fuzzy content. This demonstrates the utility and applicability of the suggested strategy.

Received: 23 September 2022 | Revised: 22 November 2022 | Accepted: 11 December 2022

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Author Contribution Statement

Muhammad Riaz: Conceptualization, Validation, Formal analysis, Supervision. Hafiz Muhammad Athar Farid: Methodology, Investigation, Writing – review & editing. Faruk Karaaslan: Software, Resources, Visualization.