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Neutrosophic Probabilistic Ordered Weighted Averaging (NPOWA) operator
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J
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DOI：10.1080/27684830.2025.2535135.png)
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Abstract
En 中文
Aggregation operators play a crucial role in decision-making by combining multiple criteria or expert opinions into a single representative value. One of the most prominent decision-making models under uncertainty is the Ordered Weighted Averaging (OWA) operator, while probabilistic aggregation focuses on risk scenarios. The Probabilistic OWA (POWA) operator offers a flexible framework that combines these two aspects through a convex combination. They cannot handle the indeterminacy and contradiction often present in real-world decision-making problems. To overcome this limitation, we propose the Neutrosophic Probabilistic Ordered Weighted Averaging (NPOWA) operator by extending the POWA operator to the neutrosophic environment. NPOWA operator facilitates decision-makers in providing an aggregate of the truth, indeterminacy, and falsity components of neutrosophic information, along with probabilistic importance and attitudinal character. We introduced the theoretical properties of the NPOWA operator, including properties such as monotonicity, idempotency, and boundedness. In addition, families of NPOWA, along with distance and similarity measures, are introduced. To solve the decision problems, two decision-making methodologies are introduced based on NPOWA aggregation and similarity measures, respectively, and further demonstrated with illustrative examples. A comparative analysis reveals that NPOWA provides a more comprehensive and adaptable decision-support framework compared to conventional OWA and POWA methods. The findings highlight its potential applications in domains requiring robust aggregation under uncertainty, such as health-care diagnostics and financial risk analysis.
Keywords:
Aggregate operator
decision-making
neutrosophic logic
OWA operator
probability
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1.1
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72
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