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Multi-attribute Group Decision Making Through the Development of Dombi Bonferroni Mean Operators Using Dual Hesitant Pythagorean Fuzzy Data

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Proceedings of Academia-Industry Consortium for Data Science

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1411))

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Abstract

Dual hesitant Pythagorean fuzzy set has been emerged as a successful fuzzy variant to deal with uncertain and vague information. Bonferroni mean possesses the capacity to deal with interrelated input arguments in multi-attribute group decision-making problems. Moreover, the process of aggregation becomes more supple using a dynamic Dombi parameter. Considering these two aspects, in this paper Bonferroni mean is merged with Dombi operations for dual hesitant Pythagorean fuzzy information processing. At first, Dombi operations on dual hesitant Pythagorean fuzzy sets are introduced. Then, utilizing Bonferroni mean operator with Dombi operations, some new aggregation operators for aggregating dual hesitant Pythagorean fuzzy information, viz., dual hesitant Pythagorean fuzzy Dombi Bonferroni mean, dual hesitant Pythagorean fuzzy Dombi geometric Bonferroni mean and their weighted operators, are proposed. Some special cases and important properties of the proposed operators have been discussed. Further, an approach for the application of the proposed operators in solving real-life group decision-making problem is presented and two illustrated numerical examples have been provided to show its validity.

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Acknowledgements

The authors express their gratitude to the reviewers for their invaluable suggestions for improvement of the quality of the manuscript.

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Deb, N., Sarkar, A., Biswas, A. (2022). Multi-attribute Group Decision Making Through the Development of Dombi Bonferroni Mean Operators Using Dual Hesitant Pythagorean Fuzzy Data. In: Gupta, G., Wang, L., Yadav, A., Rana, P., Wang, Z. (eds) Proceedings of Academia-Industry Consortium for Data Science. Advances in Intelligent Systems and Computing, vol 1411. Springer, Singapore. https://doi.org/10.1007/978-981-16-6887-6_17

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