N-Bipolar Soft Expert Sets and Their Applications in Robust Multi-Attribute Group Decision-Making
Abstract
:1. Introduction
1.1. Motivations, Objectives, and Contributions
- Proposing a novel N-BSE model that extends traditional SE set frameworks by integrating bipolarity and multinary evaluations.
- Defining and analyzing the core operations of the N-BSE model, ensuring its mathematical consistency and applicability.
- Demonstrating the practical utility of the N-BSE model in solving complex MAGDM problems, particularly those requiring nuanced assessments and expert collaboration.
- Conducting a comparative analysis between the N-BSE model and existing frameworks, highlighting its advantages and addressing its limitations.
- Introducing the N-BSE model, a hybrid set-theoretic framework that combines expert input, bipolarity, and non-binary evaluations, addressing a significant gap in MAGDM methodologies.
- Providing rigorous definitions and fundamental operations for the N-BSE model, establishing its theoretical underpinnings.
- Proposing a systematic algorithm for applying the N-BSE model to MAGDM scenarios, enabling objective and transparent DM processes.
- Demonstrating the applicability of the N-BSE model through a detailed case study on sustainable energy solutions, illustrating its potential for real-world DM.
- Offering a comprehensive comparison with existing MAGDM models, showcasing the N-BSE model’s ability to integrate expert opinions, handle bipolarity, and support multi-valued assessments effectively.
1.2. Outline of the Paper
2. Preliminary Concepts
3. N-Bipolar Soft Expert Sets
- 1.
- It is inconsistent for an expert e to evaluate an attribute p with high degrees of agreement (disagreement) and simultaneously evaluate its opposite attribute with high degrees of agreement (disagreement) for the same alternative z. Specifically, the evaluations must satisfy
- 2.
- It is not logical for an expert e to assign high degrees of agreement and disagreement simultaneously to the same attribute p for the same alternative z. The evaluations must satisfy
- One circle “∘” represents poor performance.
- One star “★” represents slightly poor performance.
- Two stars “” represent moderate performance.
- Three stars “” represent good performance.
- Four stars “” represent excellent performance.
- 0 corresponds to ∘;
- 1 corresponds to ★;
- 2 corresponds to ;
- 3 corresponds to ;
- 4 corresponds to .
- 1.
- .
- 2.
- For every and , and for every and , .
- 1.
- .
- 2.
- .
- 3.
- If and , then .
- 1.
- is the smallest N-BSE set that contains both and .
- 2.
- is the largest N-BSE set that is contained in both and .
- 1.
- = .
- 2.
- = .
- 3.
- = .
- 4.
- If , then .
- 5.
- .
- 6.
- If , then = .
- 7.
- If , then = .
- 1.
- = .
- 2.
- = .
- 3.
- = .
- 4.
- = .
- 5.
- = .
- 6.
- = .
- 1.
- = .
- 2.
- = .
- 3.
- = and = .
- 4.
- = and = .
- 5.
- = and = .
- 1.
- = .
- 2.
- = .
- 3.
- = .
- 4.
- = .
- 1.
- ⊙ = ⊙.
- 2.
- ⊙⊙ = ⊙⊙.
- 1.
- = .
- 2.
- = .
- 3.
- = .
- 4.
- = .
- 5.
- = .
- 6.
- = .
4. Application of N-Bipolar Soft Expert Sets in Multi-Attribute Group Decision-Making
4.1. Algorithm for Optimal Decision-Making
Algorithm 1 Determining the optimal choice using N-BSE sets. |
|
4.2. Case Study: Sustainable Energy Solutions
5. Comparative Analysis
5.1. Advantages of the Proposed Model
- Comprehensive evaluation: incorporates both positive and negative attributes of alternatives, offering a more balanced and complete assessment.
- Expert opinion integration: aggregates diverse evaluations from multiple experts, ensuring robust and well-rounded DM.
- Non-binary evaluation: allows multi-valued evaluations, providing finer distinctions between alternatives and enhancing DM accuracy.
- Flexibility: applicable across various domains like business, engineering, and healthcare, making it versatile for a wide range of DM problems.
- Transparency: the systematic evaluation process and expert aggregation ensure a transparent and explainable DM framework.
5.2. Comparison with Relevant Existing Approaches
5.3. Limitations of the Proposed Model
- Expert input dependency: the model relies heavily on expert evaluations, which may introduce subjectivity and prove challenging in contexts with limited expert availability.
- Complex aggregation: aggregating multi-valued expert evaluations can be complex and require sophisticated techniques to ensure consistency and accuracy, especially with large datasets.
6. Conclusions and Future Directions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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2 | 2 | 3 | 4 | 3 | |
2 | 1 | 3 | 4 | 0 | |
3 | 1 | 3 | 4 | 4 | |
2 | 1 | 3 | 1 | 2 | |
2 | 2 | 2 | 3 | 3 | |
2 | 1 | 2 | 3 | 1 | |
3 | 1 | 2 | 0 | 4 | |
2 | 2 | 3 | 3 | 3 | |
2 | 2 | 2 | 3 | 3 | |
2 | 2 | 2 | 3 | 3 | |
3 | 2 | 2 | 3 | 4 | |
0 | 2 | 0 | 1 | 0 | |
0 | 2 | 0 | 0 | 0 | |
0 | 2 | 0 | 1 | 0 | |
0 | 2 | 0 | 1 | 0 | |
1 | 2 | 1 | 0 | 0 | |
1 | 2 | 1 | 0 | 1 | |
1 | 3 | 0 | 1 | 3 | |
1 | 3 | 1 | 0 | 0 | |
2 | 0 | 1 | 2 | 0 | |
2 | 0 | 1 | 0 | 1 | |
2 | 0 | 1 | 0 | 3 | |
1 | 0 | 1 | 2 | 0 | |
1 | 1 | 1 | 0 | 0 | |
1 | 1 | 1 | 0 | 0 | |
1 | 1 | 1 | 0 | 0 | |
1 | 1 | 1 | 0 | 0 |
0 | 1 | 3 | 0 | 2 | |
1 | 2 | 1 | 0 | 3 | |
0 | 1 | 1 | 0 | 0 | |
3 | 1 | 0 | 0 | 4 | |
0 | 1 | 3 | 1 | 0 | |
1 | 1 | 1 | 3 | 0 | |
0 | 1 | 1 | 3 | 0 | |
2 | 1 | 0 | 0 | 0 | |
0 | 0 | 2 | 0 | 1 | |
1 | 0 | 1 | 0 | 1 | |
0 | 0 | 1 | 0 | 0 | |
2 | 0 | 0 | 0 | 1 | |
0 | 1 | 2 | 1 | 2 | |
1 | 2 | 1 | 3 | 3 | |
0 | 1 | 1 | 3 | 0 | |
2 | 1 | 0 | 0 | 3 | |
2 | 2 | 1 | 2 | 0 | |
3 | 2 | 3 | 2 | 1 | |
2 | 3 | 2 | 1 | 3 | |
1 | 3 | 2 | 2 | 0 | |
2 | 3 | 1 | 2 | 4 | |
3 | 3 | 3 | 0 | 4 | |
2 | 3 | 2 | 0 | 4 | |
1 | 3 | 2 | 2 | 4 | |
2 | 2 | 1 | 4 | 3 | |
3 | 2 | 3 | 4 | 3 | |
2 | 3 | 2 | 4 | 3 | |
2 | 3 | 2 | 4 | 3 | |
2 | 2 | 1 | 2 | 0 | |
3 | 2 | 3 | 0 | 1 | |
2 | 3 | 2 | 0 | 3 | |
1 | 3 | 2 | 2 | 0 |
★ | ∘ | ★ | |||||
★ | ★ | ★ | |||||
★ | ∘ | ||||||
∘ | ∘ | ||||||
∘ | ∘ | ||||||
★ | ∘ | ||||||
∘ | ★ | ★ | |||||
∘ | |||||||
★ | ∘ | ★ | ∘ | ||||
★ | ★ | ★ | ★ | ★ | |||
★ | ★ | ∘ | ★ | ||||
∘ | ★ | ∘ | ★ | ★ | ∘ | ||
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∘ | ★ | ★ | ★ | ||||
★ | ★ | ∘ | ★ | ||||
★ | ★ | ★ | |||||
★ | ∘ | ★ | ∘ | ★ | |||
∘ | ★ | ∘ | ★ | ★ | |||
★ | ★ | ∘ | ∘ | ||||
★ | ★ | ★ | |||||
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∘ | ★ | ||||||
★ | ★ | ★ | ★ | ||||
★ | ∘ | ★ | ★ | ||||
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★ | ★ | ★ | ∘ | ||||
★ | ∘ | ||||||
★ | ★ | ∘ | ★ | ∘ |
3 | 2 | 4 | 2 | 2 | 3 | 2 | |
1 | 3 | 0 | 3 | 1 | 3 | 3 | |
2 | 1 | 3 | 3 | 1 | 1 | 3 | |
3 | 2 | 2 | 2 | 1 | 3 | 0 | |
3 | 0 | 3 | 2 | 3 | 0 | 2 | |
0 | 2 | 4 | 2 | 2 | 0 | 2 | |
3 | 3 | 1 | 2 | 3 | 0 | 3 | |
2 | 0 | 3 | 1 | 1 | 3 | 2 | |
2 | 3 | 3 | 3 | 2 | 3 | 0 | |
1 | 2 | 0 | 2 | 1 | 0 | 2 | |
2 | 1 | 1 | 1 | 2 | 1 | 1 | |
1 | 3 | 1 | 0 | 3 | 2 | 1 | |
0 | 1 | 0 | 1 | 3 | 1 | 0 | |
0 | 0 | 1 | 1 | 1 | 4 | 2 | |
3 | 2 | 0 | 2 | 1 | 1 | 1 | |
1 | 1 | 3 | 2 | 0 | 3 | 1 | |
2 | 2 | 1 | 1 | 3 | 1 | 2 | |
1 | 0 | 1 | 0 | 2 | 1 | 4 | |
0 | 1 | 0 | 2 | 2 | 1 | 1 | |
2 | 1 | 3 | 1 | 2 | 0 | 0 | |
2 | 2 | 1 | 1 | 2 | 2 | 1 | |
1 | 1 | 2 | 1 | 2 | 0 | 4 | |
0 | 2 | 1 | 1 | 1 | 3 | 2 | |
4 | 2 | 0 | 2 | 1 | 2 | 2 | |
1 | 1 | 3 | 2 | 0 | 1 | 0 | |
1 | 0 | 1 | 2 | 3 | 1 | 2 | |
1 | 1 | 1 | 1 | 2 | 1 | 1 | |
3 | 2 | 0 | 2 | 1 | 3 | 2 | |
1 | 2 | 1 | 2 | 2 | 1 | 1 | |
2 | 1 | 3 | 3 | 0 | 1 | 1 | |
2 | 2 | 2 | 2 | 0 | 2 | 0 | |
4 | 2 | 2 | 2 | 2 | 0 | 1 | |
0 | 1 | 3 | 2 | 2 | 1 | 2 | |
2 | 3 | 1 | 1 | 2 | 1 | 0 | |
2 | 1 | 2 | 2 | 0 | 2 | 2 | |
2 | 2 | 1 | 1 | 0 | 1 | 0 |
3 | 2 | 4 | 2 | 2 | 3 | 2 | |
1 | 3 | 0 | 3 | 1 | 3 | 3 | |
2 | 1 | 3 | 3 | 1 | 1 | 3 | |
3 | 2 | 2 | 2 | 1 | 3 | 0 | |
3 | 0 | 3 | 2 | 3 | 0 | 2 | |
0 | 2 | 4 | 2 | 2 | 0 | 2 | |
3 | 3 | 1 | 2 | 3 | 0 | 3 | |
2 | 0 | 3 | 1 | 1 | 3 | 2 | |
2 | 3 | 3 | 3 | 2 | 3 | 0 | |
1 | 2 | 0 | 2 | 1 | 0 | 2 | |
2 | 1 | 1 | 1 | 2 | 1 | 1 | |
1 | 3 | 1 | 0 | 3 | 2 | 1 | |
0 | 1 | 0 | 1 | 3 | 1 | 0 | |
0 | 0 | 1 | 1 | 1 | 4 | 2 | |
3 | 2 | 0 | 2 | 1 | 1 | 1 | |
1 | 1 | 3 | 2 | 0 | 3 | 1 | |
2 | 2 | 1 | 1 | 3 | 1 | 2 | |
1 | 0 | 1 | 0 | 2 | 1 | 4 | |
0 | 1 | 0 | 2 | 2 | 1 | 1 | |
2 | 1 | 3 | 1 | 2 | 0 | 0 | |
2 | 2 | 1 | 1 | 2 | 2 | 1 | |
1 | 1 | 2 | 1 | 2 | 0 | 4 | |
0 | 2 | 1 | 1 | 1 | 3 | 2 | |
4 | 2 | 0 | 2 | 1 | 2 | 2 | |
1 | 1 | 3 | 2 | 0 | 1 | 0 | |
1 | 0 | 1 | 2 | 3 | 1 | 2 | |
1 | 1 | 1 | 1 | 2 | 1 | 1 | |
3 | 2 | 0 | 2 | 1 | 3 | 2 | |
1 | 2 | 1 | 2 | 2 | 1 | 1 | |
2 | 1 | 3 | 3 | 0 | 1 | 1 | |
2 | 2 | 2 | 2 | 0 | 2 | 0 | |
4 | 2 | 2 | 2 | 2 | 0 | 1 | |
0 | 1 | 3 | 2 | 2 | 1 | 2 | |
2 | 3 | 1 | 1 | 2 | 1 | 0 | |
2 | 1 | 2 | 2 | 0 | 2 | 2 | |
2 | 2 | 1 | 1 | 0 | 1 | 0 | |
Model | Expert Input | Bipolarity | Evaluation Type | Description |
---|---|---|---|---|
S-set [17] | No | Not considered | Binary | Alternatives are evaluated using predefined attributes. Alternatives are assessed in a binary manner. |
SE set [53] | Yes | Not considered | Binary | Experts assess alternatives based on binary parameters. The evaluations focus solely on whether attributes of alternatives are satisfied (1) or not (0). |
BS set [26] | No | Considered | Binary | Assesses both supportive and contradictory attributes of alternatives using binary values. |
BSE set [59] | Yes | Considered | Binary | Combines expert input with bipolarity. Experts evaluate both positive and negative aspects of alternatives, assigning binary values. |
N-S set [31] | No | Not considered | Multinary | Extends binary evaluations to multinary scales, allowing for nuanced assessments of alternatives’ attributes. |
N-SE set [56] | Yes | Not considered | Multinary | Experts evaluate alternatives using multinary scales, capturing a range of values for attributes. |
N-BS set [32] | No | Considered | Multinary | Incorporates both positive and negative evaluations of alternatives using multinary values, providing detailed assessments. |
N-BSE set (proposed) | Yes | Considered | Multinary | Combines expert input, bipolarity, and multinary evaluations. Experts assess both positive and negative aspects of alternatives using multinary values, offering comprehensive and detailed DM. |
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Musa, S.Y.; Alajlan, A.I.; Asaad, B.A.; Ameen, Z.A. N-Bipolar Soft Expert Sets and Their Applications in Robust Multi-Attribute Group Decision-Making. Mathematics 2025, 13, 530. https://doi.org/10.3390/math13030530
Musa SY, Alajlan AI, Asaad BA, Ameen ZA. N-Bipolar Soft Expert Sets and Their Applications in Robust Multi-Attribute Group Decision-Making. Mathematics. 2025; 13(3):530. https://doi.org/10.3390/math13030530
Chicago/Turabian StyleMusa, Sagvan Y., Amlak I. Alajlan, Baravan A. Asaad, and Zanyar A. Ameen. 2025. "N-Bipolar Soft Expert Sets and Their Applications in Robust Multi-Attribute Group Decision-Making" Mathematics 13, no. 3: 530. https://doi.org/10.3390/math13030530
APA StyleMusa, S. Y., Alajlan, A. I., Asaad, B. A., & Ameen, Z. A. (2025). N-Bipolar Soft Expert Sets and Their Applications in Robust Multi-Attribute Group Decision-Making. Mathematics, 13(3), 530. https://doi.org/10.3390/math13030530