Abstract
The power average (PA) operator can overcome some effects of awkward data given by predispose decision makers, and Heronian mean (HM) operator can consider the interrelationship of the aggregated arguments. In order to take the full use of these two kinds of operators, in this article, we combined the PA operator with HM operator and extended them to process linguistic neutrosophic information, and presented the linguistic neutrosophic power Heronian aggregation operator, linguistic neutrosophic power weight Heronian aggregation operator. Further, some properties of these new aggregation operators are investigated and some special cases are discussed. Furthermore, we propose new technique based on these operators for multiple attribute group decision making. Finally, an illustrative example was given to illustrate the effectiveness and advantages of the developed method by comparing with the existing method.
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Acknowledgements
This paper is supported by the National Natural Science Foundation of China (Nos. 71771140, 71471172), the Special Funds of Taishan Scholars Project of Shandong Province (No. ts201511045), Shandong Provincial Social Science Planning Project (Nos. 17BGLJ04, 16CGLJ31 and 16CKJJ27), the Teaching Reform Research Project of Undergraduate Colleges and Universities in Shandong Province (No. 2015Z057), and Key research and development program of Shandong Province (No. 2016GNC110016). The authors also would like to express appreciation to the anonymous reviewers and Editors for their very helpful comments that improved the paper.
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Appendix 1: Proof of the Theorem 2
Appendix 1: Proof of the Theorem 2
Proof
Firstly, we need to prove the following equation.
By the operational rules of LNNs defined in (6)–(9), we have
(1) When \( m = 2, \) by Eqs. (6) and (22), we have
By using Eq. (6), we get
That is, Eq. (21) holds for \( m = 2. \)
(2) Let us assume that Eq. (21) holds for \( m = z. \)
Furthermore, when \( m = z + 1 \), we have
Firstly, we prove that
We shall prove Eq. (26) on mathematical induction on \( z. \)
(a) For \( z = 2, \) we have
(b) Let us assume that Eq. (26) holds for \( z = b \), that is;
Then, when \( z = b + 1 \), we have
Therefore, Eq. (26) is true for \( z = b + 1. \) Hence, Eq. (26) is also true for all \( z. \)
Similarly, we can prove the other parts of Eq. (25).
So Eq. (25) becomes
Therefore, Eq. (21) is true for \( m = z + 1 \). Hence, Eq. (21) is true for all \( m. \)
By Eq. (21), we can prove that Eq. (20) is right. From Eq. (21) and the operational laws defined for LNNs, we have
so,
This completes the proof of Theorem 2.□
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Liu, P., Mahmood, T. & Khan, Q. Group Decision Making Based on Power Heronian Aggregation Operators Under Linguistic Neutrosophic Environment. Int. J. Fuzzy Syst. 20, 970–985 (2018). https://doi.org/10.1007/s40815-018-0450-2
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DOI: https://doi.org/10.1007/s40815-018-0450-2