Abstract
Pythagorean fuzzy sets accommodate more uncertainties than the intuitionistic fuzzy sets and hence it is one of the most important concepts to describe the fuzzy information in the process of decision making. Under this environment, the main objective of the work is to develop some new operational laws and their corresponding weighted aggregation operators. For it, we define some new neutral addition and scalar multiplication operational laws by incorporating the features of a neutral character towards the membership degrees of the set and the probability sum. Some properties of the proposed laws are investigated. Then, associated with these operational laws, we define some novel Pythagorean fuzzy weighted, ordered weighted and hybrid neutral averaging aggregation operators for Pythagorean fuzzy information, which can neutrally treat the membership and non-membership degrees. The various relations and the characteristics of the proposed operators are discussed. Further, in order to ease with the possible application, we present an algorithm to solve the multiple attribute group decision-making problems under the Pythagorean fuzzy environment. Finally, a practical example is provided to illustrate the approach and show its superiority, advantages by comparing their performance with some several existing approaches.
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References
Arora R, Garg H (2018) A robust correlation coefficient measure of dual hesistant fuzzy soft sets and their application in decision making. Eng Appl Artif Intell 72:80–92
Arora R, Garg H (2019) Group decision-making method based on prioritized linguistic intuitionistic fuzzy aggregation operators and its fundamental properties. Comput Appl Math 38(2):1–36
Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20:87–96
Chen SM, Chang CH (2016) Fuzzy multiattribute decision making based on transformation techniques of intuitionistic fuzzy values and intuitionistic fuzzy geometric averaging operators. Inf Sci 352–353:133–149
Chen TY (2019) Multiple criteria decision analysis under complex uncertainty: a pearson-like correlation-based Pythagorean fuzzy compromise approach. Int J Intell Syst 34(1):114–151
Gao H (2018) Pythagorean fuzzy hamacher prioritized aggregation operators in multiple attribute decision making. J Intell Fuzzy Syst 35(2):2229–2245
Gao H, Lu M, Wei G, Wei Y (2018) Some novel pythagorean fuzzy interaction aggregation operators in multiple attribute decision making. Fundam Inf 159(4):385–428
Garg H (2016a) Generalized intuitionistic fuzzy interactive geometric interaction operators using Einstein t-norm and t-conorm and their application to decision making. Comput Ind Eng 101:53–69
Garg H (2016b) A new generalized Pythagorean fuzzy information aggregation using Einstein operations and its application to decision making. Int J Intell Syst 31(9):886–920
Garg H (2016c) A novel correlation coefficients between Pythagorean fuzzy sets and its applications to decision-making processes. Int J Intell Syst 31(12):1234–1252
Garg H (2017a) Confidence levels based Pythagorean fuzzy aggregation operators and its application to decision-making process. Comput Math Organ Theory 23(4):546–571
Garg H (2017b) Generalized Pythagorean fuzzy geometric aggregation operators using Einstein t-norm and t-conorm for multicriteria decision-making process. Int J Intell Syst 32(6):597–630
Garg H (2017c) Novel intuitionistic fuzzy decision making method based on an improved operation laws and its application. Eng Appl Artif Intell 60:164–174
Garg H (2018a) Generalized Pythagorean fuzzy geometric interactive aggregation operators using Einstein operations and their application to decision making. J Exp Theor Artif Intell 30(6):763–794
Garg H (2018b) Hesitant Pythagorean fuzzy sets and their aggregation operators in multiple attribute decision making. Int J Uncertain Quantif 8(3):267–289
Garg H (2018c) Linguistic Pythagorean fuzzy sets and its applications in multiattribute decision-making process. Int J Intell Syst 33(6):1234–1263
Garg H (2018d) New exponential operational laws and their aggregation operators for interval-valued Pythagorean fuzzy multicriteria decision-making. Int J Intell Syst 33(3):653–683
Garg H (2019a) Intuitionistic fuzzy hamacher aggregation operators with entropy weight and their applications to multi-criteria decision-making problems. Iran J Sci Technol Trans Electr Eng 43(3):597–613
Garg H (2019b) New logarithmic operational laws and their aggregation operators for Pythagorean fuzzy set and their applications. Int J Intell Syst 34(1):82–106
Garg H (2019c) Novel neutrality operation-based Pythagorean fuzzy geometric aggregation operators for multiple attribute group decision analysis. Int J Intell Syst 34(10):2459–2489
Garg H, Arora R (2019) Generalized intuitionistic fuzzy soft power aggregation operator based on t-norm and their application in multi criteria decision-making. Int J Intell Syst 34(2):215–246
Garg H, Kumar K (2019) Linguistic interval-valued atanassov intuitionistic fuzzy sets and their applications to group decision-making problems. In: IEEE Transactions on Fuzzy Systems, pp 1–10. https://doi.org/10.1109/TFUZZ.2019.2897961
Garg H, Nancy (2019) Linguistic single-valued neutrosophic power aggregation operators and their applications to group decision-making problems. IEEE/CAA Journal of Automatic Sinica pp 1 – 13, https://doi.org/10.1109/JAS.2019.1911522
Huang JY (2014) Intuitionistic fuzzy Hamacher aggregation operator and their application to multiple attribute decision making. J Intell Fuzzy Syst 27:505–513
Jana C, Pal M, Jq Wang (2019) Bipolar fuzzy dombi aggregation operators and its application in multiple-attribute decision-making process. J Ambient Intell Humaniz Comput 10(9):3533–3549
Kaur G, Garg H (2018) Cubic intuitionistic fuzzy aggregation operators. Int J Uncertain Quantif 8(5):405–427
Kaur G, Garg H (2019) Generalized cubic intuitionistic fuzzy aggregation operators using t-norm operations and their applications to group decision-making process. Arab J Sci Eng 44(3):2775–2794
Khan MSA, Abdullah S, Ali A, Amin F, Hussain F (2019) Pythagorean hesitant fuzzy choquet integral aggregation operators and their application to multi-attribute decision-making. Soft Comput 23(1):251–267
Klir GJ, Yuan B (2005) Fuzzy sets and fuzzy logic: theory and applications. Prentice Hall of India Private Limited, New Delhi
Liang D, Xu Z (2017) The new extension of topsis method for multiple criteria decision making with hesitant Pythagorean fuzzy sets. Appl Soft Comput 60:167–179
Liu P (2017) Some frank aggregation operators for interval-valued intuitionistic fuzzy numbers and their application to group decision making. J Mult Valued Log Soft Comput 29(1–2):183–223
Ma ZM, Xu ZS (2016) Symmetric Pythagorean fuzzy weighted geometric/averaging operators and their application in multicriteria decision-making problems. Int J Intell Syst 31(12):1198–1219
Nancy Garg H (2019) A novel divergence measure and its based TOPSIS method for multi criteria decision-making under single-valued neutrosophic environment. J Intell Fuzzy Syst 36(1):101–115
Nie RX, Tian ZP, Wang JQ, Hu JH (2019) Pythagorean fuzzy multiple criteria decision analysis based on shapley fuzzy measures and partitioned normalized weighted bonferroni mean operator. Int J Intell Syst 34(2):297–324
Peng X, Yang Y (2015) Some results for Pythagorean fuzzy sets. Int J Intell Syst 30(11):1133–1160
Peng X, Dai J, Garg H (2018) Exponential operation and aggregation operator for q-rung orthopair fuzzy set and their decision-making method with a new score function. Int J Intell Syst 33(11):2255–2282
Peng XD, Garg H (2018) Algorithms for interval-valued fuzzy soft sets in emergency decision making based on WDBA and CODAS with new information measure. Comput Ind Eng 119:439–452
Rani D, Garg H (2017) Distance measures between the complex intuitionistic fuzzy sets and its applications to the decision-making process. Int J Uncertain Quantif 7(5):423–439
Rani D, Garg H (2018) Complex intuitionistic fuzzy power aggregation operators and their applications in multi-criteria decision-making. Expert Syst 35(6):e12325. https://doi.org/10.1111/exsy.12325
Viriyasitavat W (2016) Multi-criteria selection for services selection in service workflow. J Ind Inf Integr 1:20–25
Wang X, Triantaphyllou E (2008) Ranking irregularities when evaluating alternatives by using some ELECTRE methods. Omega Int J Manag Sci 36:45–63
Wei G, Zhao X, Wang H, Lin R (2013) Fuzzy power aggregation operators and their application to multiple attribute group decision making. Technol Econ Dev Econ 19(3):377–396
Wei GW, Lu M (2018) Pythagorean fuzzy power aggregation operators in multiple attribute decision maig. Int J Intell Syst 33(1):169–186
Xu LD (1988) A fuzzy multiobjective programming algorithm in decision support systems. Ann Oper Res 12(1):315–320
Xu ZS (2005) An overview of methods for determining owa weights. Int J Intell Syst 20:843–865
Xu ZS (2007) Intuitionistic fuzzy aggregation operators. IEEE Trans Fuzzy Syst 15:1179–1187
Xu ZS, Hu H (2010) Projection models for intuitionistic fuzzy multiple attribute decision making. Int J Inf Technol Decis Mak 9:267–280
Yager RR (1988) On ordered weighted avergaing aggregation operators in multi-criteria decision making. IEEE Trans Syst Man Cybern 18(1):183–190
Yager RR (2013) Pythagorean fuzzy subsets. Procedings joint IFSA world congress and NAFIPS annual meeting. Edmonton, Canada, pp 57–61
Yager RR (2014) Pythagorean membership grades in multicriteria decision making. IEEE Trans Fuzzy Syst 22(4):958–965
Yager RR, Abbasov AM (2013) Pythagorean membeship grades, complex numbers and decision making. Int J Intell Syst 28:436–452
Ye J (2017) Intuitionistic fuzzy hybrid arithmetic and geometric aggregation operators for the decision-making of mechanical design schemes. Appl Intell 47:743–751
Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353
Zeng S, Chen J, Li X (2016) A hybrid method for Pythagorean fuzzy multiple-criteria decision making. Int J Inf Technol Decis Mak 15(2):403–422
Zeng S, Mu Z, Baležentis T (2018) A novel aggregation method for Pythagorean fuzzy multiple attribute group decision making. Int J Intell Syst 33(3):573–585
Zhang X (2016a) Multicriteria Pythagorean fuzzy decision analysis: a hierarchical QUALIFLEX approach with the closeness index-based ranking. Inf Sci 330:104–124
Zhang XL (2016b) A novel approach based on similarity measure for Pythagorean fuzzy multiple criteria group decision making. Int J Intell Syst 31:593–611
Zhang XL, Xu ZS (2014) Extension of TOPSIS to multi-criteria decision making with Pythagorean fuzzy sets. Int J Intell Syst 29(12):1061–1078
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Appendix
Appendix
Proof of Proposition 2
Proof
We proof the result by using principle of mathematical induction (PMI) on \(\lambda\). For PFN \({\mathcal {A}}=( \zeta _{\mathcal {A}}, \vartheta _{\mathcal {A}})\), the following steps of the induction are executed.
- Step 1:
For \(\lambda = 2\) and by using Eq. (8), we have
$$\begin{aligned}&\text {PS}\left( \lambda \sqrt{\zeta _{\mathcal {A}}^{2}+\vartheta _{\mathcal {A}}^{2}}\right) \\&\quad = \text {PS}\left( \sqrt{\zeta _{\mathcal {A}}^{2}+\vartheta _{\mathcal {A}}^{2}}, \text {PS}\left( \sqrt{\zeta _{\mathcal {A}}^{2}+\vartheta _{\mathcal {A}}^{2}}\right) \right) \\&\quad = \text {PS}\left( \sqrt{\zeta _{\mathcal {A}}^{2}+\vartheta _{\mathcal {A}}^{2}}, \sqrt{1-\pi _{\mathcal {A}}^{2}}\right) \\&\quad = \sqrt{1-(1-\zeta _{\mathcal {A}}^{2}-\vartheta _{\mathcal {A}}^{2})(1-1+\pi _{\mathcal {A}}^{2})} \\&\quad = \sqrt{1-(\pi _{\mathcal {A}}^{2})^2} \end{aligned}$$Thus, result is true for \(\lambda =2\).
- Step 2:
Assume that result holds for \(\lambda =n\), then for \(\lambda =n+1\), we have
$$\begin{aligned}&\text {PS}\left( (n+1)\sqrt{\zeta _{\mathcal {A}}^{2}+\vartheta _{\mathcal {A}}^{2}}\right) \\&\quad = \text {PS}\left( \sqrt{\zeta _{\mathcal {A}}^{2}+\vartheta _{\mathcal {A}}^{2}}, \text {PS}\left( n\sqrt{\zeta _{\mathcal {A}}^{2}+\vartheta _{\mathcal {A}}^{2}}\right) \right) \\&\quad = \text {PS}\left( \sqrt{\zeta _{\mathcal {A}}^{2}+\vartheta _{\mathcal {A}}^{2}}, \sqrt{1-(\pi _{\mathcal {A}}^{2})^{n}}\right) \\&\quad = \sqrt{1-(1-\zeta _{\mathcal {A}}^{2}-\vartheta _{\mathcal {A}}^{2})(1-1+(\pi _{\mathcal {A}}^{2})^{n})} \\&\quad = \sqrt{1-(\pi _{\mathcal {A}}^{2})^{n+1}} \end{aligned}$$which is true for \(\lambda =n+1\). Hence, by PMI, Eq. (9) true for all \(\lambda\).
\(\square\)
Proof of Theorem 2.
Proof
-
(i)
It can be easily obtained from Eq. (12).
-
(ii)
For PFNs \({\mathcal {A}}\) and \({\mathcal {B}}\), and by Eq. (7), we get
$$\begin{aligned}&\lambda {\mathcal {A}} \oplus \lambda {\mathcal {B}} \\&\quad = \left( \begin{aligned} \sqrt{\frac{\text {MCS}^{2}(\lambda {\mathcal {A}}, \lambda {\mathcal {B}})}{\text {MCS}^{2}(\lambda {\mathcal {A}}, \lambda {\mathcal {B}}) + \text {NCS}^{2}(\lambda {\mathcal {A}}, \lambda {\mathcal {B}})} } \cdot \text {PS}\left( \text {PS}\left( \lambda \sqrt{\zeta _{\mathcal {A}}^{2}+\vartheta _{\mathcal {A}}^{2}}\right) , \text {PS}\left( \lambda \sqrt{\zeta _{\mathcal {B}}^{2}+\vartheta _{\mathcal {B}}^{2}}\right) \right) , \\ \sqrt{\frac{\text {NCS}^{2}(\lambda {\mathcal {A}}, \lambda {\mathcal {B}})}{\text {MCS}^{2}(\lambda {\mathcal {A}}, \lambda {\mathcal {B}}) + \text {NCS}^{2}(\lambda {\mathcal {A}}, \lambda {\mathcal {B}})} } \cdot \text {PS}\left( \text {PS}\left( \lambda \sqrt{\zeta _{\mathcal {A}}^{2}+\vartheta _{\mathcal {A}}^{2}}\right) , \text {PS}\left( \lambda \sqrt{\zeta _{\mathcal {B}}^{2}+\vartheta _{\mathcal {B}}^{2}}\right) \right) \end{aligned}\right) \\&\quad = \left( \begin{aligned} \sqrt{\frac{\lambda (\zeta _{\mathcal {A}}^{2}+\zeta _{\mathcal {B}}^{2})}{\lambda (\zeta _{\mathcal {A}}^{2}+\zeta _{\mathcal {B}}^{2}) + \lambda (\vartheta _{\mathcal {A}}^{2}+\vartheta _{\mathcal {B}}^{2})} } \cdot \text {PS}\left( \sqrt{1-(\pi _{\mathcal {A}}^{2})^\lambda }, \sqrt{1-(\pi _{\mathcal {B}}^{2})^\lambda }\right) , \\ \sqrt{\frac{\lambda (\vartheta _{\mathcal {A}}^{2}+\vartheta _{\mathcal {B}}^{2})}{\lambda (\zeta _{\mathcal {A}}^{2}+\zeta _{\mathcal {B}}^{2}) + \lambda (\vartheta _{\mathcal {A}}^{2}+\vartheta _{\mathcal {B}}^{2})} } \cdot \text {PS}\left( \sqrt{1-(\pi _{\mathcal {A}}^{2})^{\lambda }}, \sqrt{1-(\pi _{\mathcal {B}}^{2})^{\lambda }}\right) \end{aligned} \right) \nonumber \\&\quad = \left( \begin{aligned} \sqrt{\frac{\zeta _{\mathcal {A}}^{2}+\zeta _{\mathcal {B}}^{2}}{ \zeta _{\mathcal {A}}^{2}+\zeta _{\mathcal {B}}^{2} + \vartheta _{\mathcal {A}}^{2}+\vartheta _{\mathcal {B}}^{2}} \left( 1-(\pi _{\mathcal {A}}^{2})^\lambda (\pi _{\mathcal {B}}^{2})^\lambda \right) }, \\ \sqrt{\frac{\vartheta _{\mathcal {A}}^{2}+\vartheta _{\mathcal {B}}^{2}}{\zeta _{\mathcal {A}}^{2}+\zeta _{\mathcal {B}}^{2} + \vartheta _{\mathcal {A}}^{2}+\vartheta _{\mathcal {B}}^{2}} \left( 1-(\pi _{\mathcal {A}}^{2})^{\lambda }(\pi _{\mathcal {B}}^{2})^{\lambda }\right) } \end{aligned} \right) \\&\quad = \left( \begin{aligned} \sqrt{\frac{\zeta _{\mathcal {A}}^{2} + \zeta _{\mathcal {B}}^{2}}{\zeta _{\mathcal {A}}^{2}+\vartheta _{\mathcal {A}}^{2}+\zeta _{\mathcal {B}}^{2}+\vartheta _{\mathcal {B}}^{2}}}\cdot \text {PS}\left( \lambda \left( \sqrt{1-\pi _{\mathcal {A}}^{2}\pi _{\mathcal {B}}^{2}}\right) \right) , \\ \sqrt{\frac{\vartheta _{\mathcal {A}}^{2}+\vartheta _{\mathcal {B}}^{2}}{\zeta _{\mathcal {A}}^{2}+\vartheta _{\mathcal {A}}^{2}+\zeta _{\mathcal {B}}^{2}+\vartheta _{\mathcal {B}}^{2}}} \cdot PS\left( \lambda \left( \sqrt{1-\pi _{\mathcal {A}}^{2}\pi _{\mathcal {B}}^{2}}\right) \right) \end{aligned}\right) \\&\quad = \lambda ({\mathcal {A}} \oplus {\mathcal {B}}) \end{aligned}$$ -
(iii)
For two positive real numbers \(\lambda _1\) and \(\lambda _2\), we have
$$\begin{aligned}&\lambda _1 {\mathcal {A}} \oplus \lambda _2 {\mathcal {A}} \\&\quad = \left( \begin{aligned} \sqrt{\frac{\text {MCS}^{2}(\lambda _1 {\mathcal {A}}, \lambda _2 {\mathcal {A}})}{\text {MCS}^{2}(\lambda _1 {\mathcal {A}}, \lambda _2 {\mathcal {A}}) + \text {NCS}^{2}(\lambda _1 {\mathcal {A}}, \lambda _2 {\mathcal {A}})} } \cdot \text {PS}\left( PS\left( \lambda _1 \sqrt{\zeta _{\mathcal {A}}^{2}+\vartheta _{\mathcal {A}}^{2}}\right) , \text {PS}\left( \lambda _2 \sqrt{\zeta _{\mathcal {A}}^{2}+\vartheta _{\mathcal {A}}^{2}}\right) \right) , \\ \sqrt{\frac{\text {NCS}^{2}(\lambda _1 {\mathcal {A}}, \lambda _2 {\mathcal {A}})}{\text {MCS}^{2}(\lambda _1 {\mathcal {A}}, \lambda _2 {\mathcal {A}}) + \text {NCS}^{2}(\lambda _1A, \lambda _2 {\mathcal {A}})} } \cdot \text {PS} \left( \text {PS}\left( \lambda _1 \sqrt{\zeta _{\mathcal {A}}^{2}+\vartheta _{\mathcal {A}}^{2}}\right) , \text {PS}\left( \lambda _2\sqrt{\zeta _{\mathcal {A}}^{2}+\vartheta _{\mathcal {A}}^{2}}\right) \right) \end{aligned} \right) \\&\quad = \left( \begin{aligned} \sqrt{\frac{(\lambda _1+\lambda _2) \zeta _{\mathcal {A}}^{2}}{(\lambda _1+\lambda _2) \zeta _{\mathcal {A}}^{2} + (\lambda _1+\lambda _2) \vartheta _{\mathcal {A}}^{2}} } \cdot \text {PS}\left( \sqrt{1-(\pi _{\mathcal {A}}^{2})^{\lambda _1}}, \sqrt{1-(\pi _{\mathcal {A}}^{2})^{\lambda _2}}\right) , \\ \sqrt{\frac{(\lambda _1+\lambda _2)\vartheta _{\mathcal {A}}^{2}}{(\lambda _1+\lambda _2) \zeta _{\mathcal {A}}^{2} + (\lambda _1+\lambda _2)\vartheta _{\mathcal {A}}^{2}} } \cdot \text {PS} \left( \sqrt{1-(\pi _{\mathcal {A}}^{2})^{\lambda _1}}, \sqrt{1-(\pi _{\mathcal {A}}^{2})^{\lambda _2}}\right) \end{aligned} \right) \\&\quad = \left( \sqrt{\frac{\zeta _{\mathcal {A}}^{2}}{ \zeta _{\mathcal {A}}^{2}+\vartheta _{\mathcal {A}}^{2}} \cdot \left( 1-(\pi _{\mathcal {A}}^{2})^{\lambda _1+\lambda _2}\right) }, \sqrt{\frac{\vartheta _{\mathcal {A}}^{2}}{\zeta _{\mathcal {A}}^{2}+\vartheta _{\mathcal {A}}^{2}} \cdot \left( 1-(\pi _{\mathcal {A}}^{2})^{\lambda _1+\lambda _2} \right) } \right) \\&\quad = (\lambda _1+\lambda _2){\mathcal {A}} \end{aligned}$$
\(\square\)
Proof of Theorem 3
Proof
For PFNs \({\mathcal {A}}_i(i=1,2,\ldots ,n)\) and real numbers \(\omega _i>0\), the first result holds immediately from Theorem 1. Now, in order to show Eq. (15) holds, we follows the steps of the PMI on n which are summarized as follows:
- Step 1:
For \(n=1\), we have \({\mathcal {A}}_i=( \zeta _i, \vartheta _i)\) and \(\omega _i=1\). Thus, we can write as
$$\begin{aligned} \text {PFWNA}({\mathcal {A}}_1)= & {} \omega _1{\mathcal {A}}_1 \\= & {} \big ( \zeta _{1},\vartheta _{1}\big ) \\= & {} \left( \sqrt{\frac{\omega _1 \zeta _{1}^{2}}{ \omega _1(\zeta _{1}^{2}+\vartheta _{1}^{2})} \left( 1-\left( \pi _1^{2}\right) ^{\omega _1}\right) }, \sqrt{\frac{\omega _1 \vartheta _{1}^{2}}{\omega _1(\zeta _{1}^{2}+\vartheta _{1}^{2})}\left( 1-\left( \pi _1^{2}\right) ^{\omega _1}\right) }\right) \end{aligned}$$Thus, Eq.(15) holds.
- Step 2:
Assume that the Eq.(15) holds for \(n=k\), that is
$$\begin{aligned}&\text {PFWNA}({\mathcal {A}}_1,{\mathcal {A}}_2,\ldots ,{\mathcal {A}}_k) \\&\quad = \left( \begin{aligned} \sqrt{\frac{\sum \nolimits _{i=1}^k \omega _i(\zeta _{i}^{2})}{\sum \nolimits _{i=1}^k \omega _i(\zeta _{i}^{2}+\vartheta _{i}^{2})} \cdot \left( 1-\prod \limits _{i=1}^k \left( \pi _{i}^{2}\right) ^{\omega _i}\right) }, \sqrt{\frac{\sum \nolimits _{i=1}^k \omega _i \vartheta _{i}^{2}}{\sum \nolimits _{i=1}^k \omega _i(\zeta _{i}^{2}+\vartheta _{i}^{2})} \cdot \left( 1-\prod \limits _{i=1}^k\left( \pi _{i}^{2}\right) ^{\omega _i}\right) } \end{aligned}\right) \end{aligned}$$Now, for \(n=k+1\), we have
$$\begin{aligned}&\text {PFWNA}({\mathcal {A}}_1,{\mathcal {A}}_2,\ldots ,{\mathcal {A}}_{k+1}) \\&\quad = \text {PFWNA}({\mathcal {A}}_1,{\mathcal {A}}_2,\ldots ,{\mathcal {A}}_k) \oplus (\omega _{k+1}{\mathcal {A}}_{k+1}) \\&\quad = \left( \begin{aligned} \sqrt{\frac{\text {MCS}^{2}(\text {PFWNA}({\mathcal {A}}_1,{\mathcal {A}}_2,\ldots ,{\mathcal {A}}_k),\omega _{k+1}{\mathcal {A}}_{k+1})}{ \left( \begin{aligned} & \text {MCS}^{2}(\text {PFWNA}({\mathcal {A}}_1,\ldots ,{\mathcal {A}}_k),\omega _{k+1}{\mathcal {A}}_{k+1}) \\ & + \text {NCS}^{2}(\text {PFWNA}({\mathcal {A}}_1,\ldots ,{\mathcal {A}}_k),\omega _{k+1}{\mathcal {A}}_{k+1}) \end{aligned} \right) } } \cdot \text {PS}\left( \sqrt{1-\prod \limits _{i=1}^k(\pi _{i}^{2})^{\omega _i}}, \sqrt{1-(\pi _{k+1}^{2})^{\omega _{k+1}}} \right) , \\ \sqrt{\frac{\text {NCS}^{2}(\text {PFWNA}({\mathcal {A}}_1,{\mathcal {A}}_2\ldots ,{\mathcal {A}}_k), \omega _{k+1}{\mathcal {A}}_{k+1})}{ \left( \begin{aligned} \text {MCS}^{2}(\text {PFWNA}({\mathcal {A}}_1,\ldots ,{\mathcal {A}}_k), \omega _{k+1}{\mathcal {A}}_{k+1}) \\ + \text {NCS}^{2}(\text {PFWNA}({\mathcal {A}}_1,\ldots ,{\mathcal {A}}_k), \omega _{k+1}{\mathcal {A}}_{k+1}) \end{aligned} \right) }} \cdot \text {PS}\left( \sqrt{1-\prod \limits _{i=1}^k(\pi _i^{2})^{\omega _i}}, \sqrt{1-(\pi _{k+1}^{2})^{\omega _{k+1}}}\right) \end{aligned} \right) \end{aligned}$$By definition of MCS and NCS, we have \(\text {MCS}^{2}(\text {PFWNA}({\mathcal {A}}_1,\ldots ,{\mathcal {A}}_k), \omega _{k+1}{\mathcal {A}}_{k+1}) = \text {MCS}^{2}(\text {PFWNA}({\mathcal {A}}_1,\ldots ,{\mathcal {A}}_k)) + \text {MCS}^{2}(\omega _{k+1}{\mathcal {A}}_{k+1})\)\(=\sum \nolimits _{i=1}^k \omega _i \zeta _i^{2} + \omega _{k+1}\zeta _{k+1}^{2}\)\(=\sum \limits _{i=1}^{k+1} \omega _i \zeta _i^{2}\). Similarly, we get
$$\begin{aligned}&\text {NCS}^{2}(\text {PFWNA}({\mathcal {A}}_1,\ldots ,{\mathcal {A}}_k), \omega _{k+1}{\mathcal {A}}_{k+1}) = \sum \limits _{i=1}^{k+1} \omega _i \vartheta _i^{2} \end{aligned}$$Also by definition of PS, we have
$$\begin{aligned}&\text {PS}\left( \sqrt{1-\prod \limits _{i=1}^k (\pi _i^{2})^{\omega _i}}, \sqrt{1-(\pi _{k+1}^{2})^{\omega _{k+1}}} \right) \\&\quad = \sqrt{1-\left( 1-1+\prod \limits _{i=1}^k (\pi _i^{2})^{\omega _i}\right) \left( 1-1+(\pi _{k+1}^{2})^{\omega _{k+1}}\right) } \\&\quad = \sqrt{1-\prod \limits _{i=1}^{k+1} (\pi _i^{2})^{\omega _i}} \end{aligned}$$Thus,
$$\begin{aligned}&\text {PFWNA}({\mathcal {A}}_1,{\mathcal {A}}_2,\ldots ,{\mathcal {A}}_{k+1}) \\&\quad = \left( \begin{aligned} \sqrt{\frac{\sum \nolimits _{i=1}^{k+1} \omega _i \zeta _{i}^{2} }{\sum \nolimits _{i=1}^{k+1} \omega _i(\zeta _{i}^{2}+\vartheta _{i}^{2})} \cdot \left( 1-\prod \limits _{i=1}^{k+1}(\pi _{i}^{2})^{\omega _i}\right) }, \sqrt{\frac{\sum \nolimits _{i=1}^{k+1} \omega _i \vartheta _{i}^{2}}{\sum \nolimits _{i=1}^{k+1} \omega _i(\zeta _{i}^{2}+\vartheta _{i}^{2})}\cdot \left( 1-\prod \limits _{i=1}^{k+1}(\pi _{i}^{2})^{\omega _i}\right) } \end{aligned} \right) \end{aligned}$$i.e., Eq. (15) holds for \(n=k+1\). Therefore, by PMI, Eq.(15) holds for all n, which completes the theorem.
\(\square\)
Proof of Theorem 4
Proof
For a collection of PFNs \({\mathcal {A}}_i=( \zeta _i, \vartheta _i)\) and \({\mathcal {A}}_0=( \zeta _0, \vartheta _0)\) such that \({\mathcal {A}}_i = {\mathcal {A}}_0\) we have \(\zeta _i = \zeta _0\) and \(\vartheta _i = \vartheta _0\) for all i. Then, by Eq. (15) and by weight vector \(\omega _i>0\) with \(\sum \nolimits _{i=1}^n \omega _i=1\), we have
\(\square\)
Proof of Theorem 5.
Proof
For a collection of PFNs \({\mathcal {A}}_i=( \zeta _i, \vartheta _i) (i=1,2,\ldots ,n)\), we have
- (i)$$\begin{aligned}&\min \limits _i\big \{\zeta _{i}^{2} + \vartheta _{i}^{2} \big \} = 1-\left( 1-\min \limits _i\left\{ \zeta _{i}^{2} + \vartheta _{i}^{2} \right\} \right) ^{\sum \limits _{i=1}^n \omega _i}\\&\quad = 1-\prod \limits _{i=1}^n \left( 1-\min \limits _i\left\{ \zeta _{i}^{2} + \vartheta _{i}^{2} \right\} \right) ^{\omega _i} \le 1 - \prod \limits _{i=1}^n \left( 1-\zeta _{i}^{2} - \vartheta _{i}^{2} \right) ^{\omega _i} \\&\quad \le 1 - \prod \limits _{i=1}^n \left( 1-\max \limits _i\left\{ \zeta _{i}^{2} + \vartheta _{i}^{2} \right\} \right) ^{\omega _i} \\&\quad = 1 - \left( 1-\max \limits _i\left\{ \zeta _{i}^{2} + \vartheta _{i}^{2} \right\} \right) ^{\sum \limits _{i=1}^n \omega _i} = \max \limits _i\left\{ \zeta _{i}^{2} + \vartheta _{i}^{2} \right\} \end{aligned}$$
Thus, we have
$$\begin{aligned}&\min \limits _i\big \{\zeta _{i}^{2} + \vartheta _{i}^{2} \big \} \le 1 - \prod \limits _{i=1}^n \left( 1-\zeta _{i}^{2} - \vartheta _{i}^{2} \right) ^{\omega _i} \\&\quad \le \max \limits _i\left\{ \zeta _{i}^{2} + \vartheta _{i}^{2} \right\} \end{aligned}$$Now, by Theorem 15, we get
$$\begin{aligned} \zeta _P= & {} \sqrt{\frac{\sum \nolimits _{i=1}^n \omega _i(\zeta _{i}^{2})}{\sum \nolimits _{i=1}^n \omega _i(\zeta _{i}^{2}+\vartheta _{i}^{2})} \cdot \left( 1-\prod \limits _{i=1}^n\left( \pi _{i}^{2}\right) ^{\omega _i}\right) } \quad \text { and } \\ \vartheta _P= & {} \sqrt{\frac{\sum \nolimits _{i=1}^n \omega _i \vartheta _{i}^{2}}{\sum \nolimits _{i=1}^n \omega _i(\zeta _{i}^{2}+\vartheta _{i}^{2})} \cdot \left( 1-\prod \limits _{i=1}^n\left( \pi _{i}^{2}\right) ^{\omega _i}\right) } \end{aligned}$$Therefore,
$$\begin{aligned} \zeta _P^{2} + \vartheta _P^{2} = 1 - \prod \limits _{i=1}^n \left( 1-\zeta _{i}^{2} - \vartheta _{i}^{2} \right) ^{\omega _i} \end{aligned}$$Hence, we get \(\min \limits _i\big \{\zeta _{i}^{2}+\vartheta _{i}^{2}\big \} \le \zeta _{P}^{2}+ \vartheta _{P}^{2} \le \max \limits _i\big \{\zeta _{i}^{2} + \vartheta _{i}^{2} \big \}\).
- (ii)
Since, \(\zeta _i\ge \min \limits _i \{\zeta _i\}\), so by expression of \(\zeta _P\) we have
$$\begin{aligned} \zeta _P^{2}\ge & {} \frac{\sum \nolimits _{i=1}^n \omega _i(\min \limits _i \{\zeta _{i}^{2}\})}{\sum \nolimits _{i=1}^n \omega _i(\max \limits _i \{\zeta _{i}^{2}+\vartheta _{i}^{2}\})} \left[ 1-\prod \limits _{i=1}^n(1-\min \limits _i \{\zeta _i^{2}+\vartheta _i^{2}\})^{\omega _i} \right] \\= & {} \frac{\min \limits _i \{\zeta _{i}^{2}\}}{\max \limits _i \{\zeta _{i}^{2}+\vartheta _{i}^{2}\}} \left[ 1-(1-\min \limits _i \{\zeta _i^{2}+\vartheta _i^{2}\})^{\sum \nolimits _{i=1}^n \omega _i}\right] \\= & {} \frac{\min \limits _i \{\zeta _i^{2}+\vartheta _i^{2}\} \min \limits _i \{\zeta _{i}^{2}\}}{\max \limits _i \{\zeta _{i}^{2}+\vartheta _{i}^{2}\}} \end{aligned}$$Moreover,
$$\begin{aligned} \zeta _P^{2}\le & {} \frac{\sum \nolimits _{i=1}^n \omega _i(\max \limits _i \{\zeta _{i}^{2}\})}{\sum \nolimits _{i=1}^n \omega _i(\min \limits _i \{\zeta _{i}^{2}+\vartheta _{i}^{2}\})} \left[ 1-\prod \limits _{i=1}^n(1-\max \limits _i \{\zeta _i^{2}+\vartheta _i^{2}\})^{\omega _i} \right] \\= & {} \frac{\max \limits _i \{\zeta _{i}^{2}\}}{\min \limits _i \{\zeta _{i}^{2}+\vartheta _{i}^{2}\}} \left[ 1-(1-\max \limits _i \{\zeta _i^{2}+\vartheta _i^{2}\})^{\sum \nolimits _{i=1}^n \omega _i}\right] \\= & {} \frac{\max \limits _i \{\zeta _i^{2}+\vartheta _i^{2}\} \max \limits _i \{\zeta _{i}^{2}\}}{\min \limits _i \{\zeta _{i}^{2}+\vartheta _{i}^{2}\}} \end{aligned}$$Also, by definition of PFN and Theorem 3, we get \(\zeta _p^{2}\le 1\). Thus, we have
$$\begin{aligned}&\frac{\min \limits _i\big \{\zeta _{i}^{2}+\vartheta _{i}^{2}\big \}\cdot \min \limits _i\big \{\zeta _{i}^{2}\big \}}{\max \limits _i\big \{\zeta _{i}^{2}+\vartheta _{i}^{2}\big \}} \le \zeta _P^{2} \\&\quad \le \min \limits _i\Bigg \{\frac{\max \limits _i \big \{\zeta _{i}^{2}+\vartheta _{i}^{2}\big \}\cdot \max \limits _i\big \{\zeta _{i}^{2}\big \}}{\min \limits _i\big \{\zeta _{i}^{2} + \vartheta _{i}^{2}\big \}},1\Bigg \} \end{aligned}$$ - (iii)
As similar to part (ii), we can obtain it. So, we omit here.
\(\square\)
Proof of Theorem 6
Proof
For a collection of PFNs \({\mathcal {A}}_1,{\mathcal {A}}_2,\ldots ,{\mathcal {A}}_n\) and \({\mathcal {B}}_1,{\mathcal {B}}_2,\ldots ,{\mathcal {B}}_n\) and by using Theorem 3, we get \(\text {PFWNA}\)\(({\mathcal {A}}_1,{\mathcal {A}}_2,\ldots ,{\mathcal {A}}_n) = ( \zeta _{p_A}, \vartheta _{P_{\mathcal {A}}})\) and \(\text {PFWNA}({\mathcal {B}}_1,{\mathcal {B}}_2,\ldots ,{\mathcal {B}}_n) = ( \zeta _{p_B}, \vartheta _{P_{\mathcal {B}}})\) where
Based on these information, we have
- (i)
If \(\zeta _{{\mathcal {A}}_i}^{2} + \vartheta _{{\mathcal {A}}_i}^{2} \le \zeta _{{\mathcal {B}}_i}^{2} + \vartheta _{{\mathcal {B}}_i}^{2}\), then we have \(\zeta _{P_{\mathcal {A}}}^{2} + \vartheta _{P_{\mathcal {A}}}^{2} \le 1-\prod \limits _{i=1}^n(1-\zeta _{{\mathcal {A}}_i}^{2}-\vartheta _{{\mathcal {A}}_i}^{2})^{\omega _i}\)\(\le\)\(1-\prod \limits _{i=1}^n(1-\zeta _{{\mathcal {B}}_i}^{2}-\vartheta _{{\mathcal {B}}_i}^{2})^{\omega _i}\)\(=\zeta _{P_{\mathcal {B}}}^{2} + \vartheta _{P_{\mathcal {B}}}^{2}\).
- (ii)
If \(\zeta _{{\mathcal {A}}_i}^{2} + \vartheta _{{\mathcal {A}}_i}^{2} = \zeta _{{\mathcal {B}}_i}^{2} + \vartheta _{{\mathcal {B}}_i}^{2}\), and \(\zeta _{{\mathcal {A}}_i}\le \zeta _{{\mathcal {B}}_i}\), then we have
$$\begin{aligned} \zeta _{P_{\mathcal {A}}}^{2}= & {} \frac{\sum \nolimits _{i=1}^n \omega _i(\zeta _{{\mathcal {A}}_i}^{2})}{\sum \nolimits _{i=1}^n \omega _i(\zeta _{{\mathcal {A}}_i}^{2}+\vartheta _{{\mathcal {A}}_i}^{2})}\left[ 1-\prod \limits _{i=1}^n(1-\zeta _{{\mathcal {A}}_i}^{2}-\vartheta _{{\mathcal {A}}_i}^{2})^{\omega _i}\right] \\\le & {} \frac{\sum \nolimits _{i=1}^n \omega _i(\zeta _{{\mathcal {B}}_i}^{2})}{\sum \nolimits _{i=1}^n \omega _i(\zeta _{{\mathcal {B}}_i}^{2}+\vartheta _{{\mathcal {B}}_i}^{2})}\left[ 1-\prod \limits _{i=1}^n(1-\zeta _{{\mathcal {B}}_i}^{2}-\vartheta _{{\mathcal {B}}_i}^{2})^{\omega _i}\right] \\= & {} \zeta _{P_{\mathcal {B}}}^{2} \end{aligned}$$and
$$\begin{aligned} \vartheta _{P_{\mathcal {A}}}^{2}= & {} \frac{\sum \nolimits _{i=1}^n \omega _i(\vartheta _{{\mathcal {A}}_i}^{2})}{\sum \nolimits _{i=1}^n \omega _i(\zeta _{{\mathcal {A}}_i}^{2}+\vartheta _{{\mathcal {A}}_i}^{2})}\left[ 1-\prod \limits _{i=1}^n(1-\zeta _{{\mathcal {A}}_i}^{2}-\vartheta _{{\mathcal {A}}_i}^{2})^{\omega _i}\right] \\\ge & {} \frac{\sum \nolimits _{i=1}^n \omega _i(\vartheta _{{\mathcal {B}}_i}^{2})}{\sum \nolimits _{i=1}^n \omega _i(\zeta _{{\mathcal {B}}_i}^{2}+\vartheta _{{\mathcal {B}}_i}^{2})}\left[ 1-\prod \limits _{i=1}^n(1-\zeta _{{\mathcal {B}}_i}^{2}-\vartheta _{{\mathcal {B}}_i}^{2})^{\omega _i}\right] \\= & {} \vartheta _{P_{\mathcal {B}}}^{2} \end{aligned}$$Hence, the result.
- (iii)
From part (ii), we obtain that \(\zeta _{P_{\mathcal {A}}}^{2} \le \zeta _{P_{\mathcal {B}}}^{2}\) and \(\vartheta _{P_{\mathcal {A}}}^{2} \ge \vartheta _{P_{\mathcal {B}}}^{2}\). Therefore, by definition of score function given in Eq. (2), we get \(\zeta _{P_{\mathcal {A}}}^{2} -\vartheta _{P_{\mathcal {A}}}^{2} \le \zeta _{P_{\mathcal {B}}}^{2} - \vartheta _{P_{\mathcal {B}}}^{2}\). Hence, based on an order relation between PFNs, we have \(\text {PFWNA}({\mathcal {A}}_1,{\mathcal {A}}_2,\ldots ,{\mathcal {A}}_n) \le \text {PFWNA}({\mathcal {B}}_1,{\mathcal {B}}_2,\ldots ,{\mathcal {B}}_n)\).
\(\square\)
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Garg, H. Neutrality operations-based Pythagorean fuzzy aggregation operators and its applications to multiple attribute group decision-making process. J Ambient Intell Human Comput 11, 3021–3041 (2020). https://doi.org/10.1007/s12652-019-01448-2
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DOI: https://doi.org/10.1007/s12652-019-01448-2