Document Type : Research Article
Authors
Department of Industrial Engineering, Payame Noor University, Iran.
10.30473/coam.2025.74715.1311
Abstract
This paper addresses multi-attribute group decision-making (MAGDM) where linguistic assessments are represented by both positive and negative interval type-2 fuzzy numbers (IT2FNs), capturing the intrinsic uncertainty of group evaluations more accurately. We introduce a novel ranking method for IT2FNs that simultaneously utilizes the mean and standard deviation of the upper and lower membership functions, as well as the IT2FN's height. This enhances its discriminatory capability. The theoretical foundations of this ranking— encompassing zero, unity, and symmetry properties— are rigorously established, and its superiority over existing techniques is demonstrated through comparative analyses on seven benchmark datasets. Building on this ranking, we develop an integrated fuzzy MAGDM framework that can handle both positive and negative IT2FN assessments for criteria and weights. The framework’s practicality and effectiveness are validated through two case studies: one with exclusively positive linguistic terms and another with mixed positive and negative scales. Results indicate that the proposed ranking and decision framework yield more rational and robust group decisions under substantial uncertainty. They outperform conventional fuzzy methods and offer a nuanced solution for real-world MAGDM scenarios.
Highlights
- Novel ranking method for IT2FNs (positive, negative, and neutral) that jointly uses mean, standard deviation, and height.
- Enhances MADM/MAGDM with bipolar (positive–negative) linguistic scales and neutral assessments.
- Integrated MAGDM framework capable of processing dual-scale IT2FN criteria and weights.
- Demonstrated superiority over state-of-the-art methods via comprehensive benchmark comparisons.
- Real-world relevance: improved rationality and robustness in complex, uncertainty-rich decision problems.
Keywords
Main Subjects
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