Bipolar linear Diophantine fuzzy EDAS for multi-attribute group decision-making

Volume 38, Issue 1, pp 25--44 https://dx.doi.org/10.22436/jmcs.038.01.03

Publication Date: November 21, 2024 Submission Date: July 17, 2024 Revision Date: September 15, 2024 Accteptance Date: September 29, 2024

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Authors

J. Vimala - Department of Mathematics, Alagappa University, Karaikudi, Tamilnadu, India. S. Nithya Sri - Department of Mathematics, Alagappa University, Karaikudi, Tamilnadu, India. N. Kausar - Department of Mathematics, Faculty of Arts and Science, Yildiz Technical University, Esenler, 34220, Istanbul, Turkey. B. Vrioni - School of Arts and Sciences, American International University, Kuwait. D. Pamucar - Department of Operations Research and Statistics, Faculty of Organizational Sciences, University of Belgrade, Belgrade, Serbia. - Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan City, 320315, Taiwan. - Department of Mechanics and Mathematics, Western Caspian University, Baku, Azerbaijan. V. Simic - University of Belgrade, Faculty of Transport and Traffic Engineering, Vojvode Stepe 305, Belgrade, 11010, Serbia. - Department of Computer Science and Engineering, College of Informatics, Korea University, Seoul, 02841, Republic of Korea.

Abstract

The formation of new properties for Bipolar Linear Diophantine Fuzzy Set (\( { {\mathcal{BLDFS}}}\)) enhances the evaluation procedure by including control parameters and meticulously accumulating positive as well as negative opinions. While previous approaches encounter difficulties in accurately grasping uncertainties, the design and development of a novel Bipolar Linear Diophantine Fuzzy Evaluation based on Distance from Average Solution (\( { {\mathcal{BLDFEDAS}}}\)) approach aim to bridge the gap in the existing research. An application of implementing this approach is presented by case analysis on choosing the best Forensic Decision Intelligence (\( { {\mathcal{FDI}}}\)) system, as the significance of forensic science in the court system has yet to be demonstrated. Further, a novel Bipolar Linear Diophantine Fuzzy Multi-Attributive Border Approximation Area Comparison (\( { {\mathcal{BLDFMABAC}}}\)) methodology is provided to uplift the effectiveness of results by \( { {\mathcal{BLDFEDAS}}}\). By rigorously incorporating control parameters and bipolarity, the field substantially progresses and establishes a robust framework for tackling ambiguity in decision support systems.

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Keywords

• Bipolar linear Diophantine fuzzy set
• evaluation based on distance from average solution
• multi-attributive border approximation area comparison
• multi-attribute group decision-making

MSC

•  03B52
•  03E72
•  08A72

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