Abstract
Pythagorean fuzzy set, initially extended by Yager from intuitionistic fuzzy set, is capable of modeling information with more uncertainties in the process of multi-criteria decision making (MCDM), thus can be used on wider range of conditions. The fuzzy decision analysis of this paper is mainly built upon two expressions in Pythagorean fuzzy environment, named Pythagorean fuzzy number (PFN) and interval-valued Pythagorean fuzzy number (IVPFN), respectively. We initiate a novel axiomatic definition of Pythagorean fuzzy distance measurement, including PFNs and IVPFNs. After that, corresponding theorems are put forward and then proved. Based on the defined distance measurements, the closeness indexes are developed for both expressions, inspired by the idea of technique for order preference by similarity to ideal solution (TOPSIS) approach. After these basic definitions have been established, the hierarchical decision approach is presented to handle MCDM problems under Pythagorean fuzzy environment. To address hierarchical decision issues, the closeness index-based score function is defined to calculate the score of each permutation for the optimal alternative. To determine criterion weights, a new method based on the proposed similarity measure and aggregation operator of PFNs and IVPFNs is presented according to Pythagorean fuzzy information from decision matrix, rather than being provided in advance by decision makers, which can effectively reduce human subjectivity. An experimental case is then conducted to demonstrate the applicability and flexibility of the proposed decision approach. Finally, extension forms of Pythagorean fuzzy decision approach for heterogeneous information are briefly introduced to show its potentials on further applications in other processing fields with information uncertainties.
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References
Ali Khan MS, Abdullah S, Ali A (2019) Multiattribute group decision-making based on pythagorean fuzzy einstein prioritized aggregation operators. Int J Intell Syst 34(5):1001–1033
Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20(1):87–96
Biswas SK, Devi D, Chakraborty M (2018) A hybrid case based reasoning model for classification in internet of things (IoT) environment. J Organ End User Comput (JOEUC) 30(4):104–122
Chen TY (2018) An outranking approach using a risk attitudinal assignment model involving pythagorean fuzzy information and its application to financial decision making. Appl Soft Comput 71:460–487
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
Cui H, Liu Q, Zhang J, Kang B (2019) An improved deng entropy and its application in pattern recognition. IEEE Access 7:18,284–18,292
Dick S, Yager RR, Yazdanbakhsh O (2016) On pythagorean and complex fuzzy set operations. IEEE Trans Fuzzy Syst 24(5):1009–1021
Dong Y, Zhang J, Li Z, Hu Y, Deng Y (2019) Combination of evidential sensor reports with distance function and belief entropy in fault diagnosis. Int J Comput Commun Control 14(3):329–343
Du Y, Hou F, Zafar W, Yu Q, Zhai Y (2017) A novel method for multiattribute decision making with interval-valued pythagorean fuzzy linguistic information. Int J Intell Syst 32(10):1085–1112
Fei L (2019) On interval-valued fuzzy decision-making using soft likelihood functions. Int J Intell Syst 34 (7):1631–1652
Fei L, Deng Y (2019) A new divergence measure for basic probability assignment and its applications in extremely uncertain environments. Int J Intell Syst 34(4):584–600
Fei L, Deng Y, Hu Y (2019) DS-VIKOR: a new multi-criteria decision-making method for supplier selection. Int J Fuzzy Syst 21(1):157–175
Fei L, Wang H, Chen L, Deng Y (2019) A new vector valued similarity measure for intuitionistic fuzzy sets based on owa operators. Iranian J Fuzzy Syst 16(3):113–126
Gao X, Deng Y (2019) The generalization negation of probability distribution and its application in target recognition based on sensor fusion. Int J Distrib Sens Netw 15(5), https://doi.org/10.1177/1550147719849,381
Gao X, Deng Y (2019) The negation of basic probability assignment. IEEE Access 7(1), https://doi.org/10.1109/ACCESS.2019.2901,932
Garg H (2017) 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 (2017) A new improved score function of an interval-valued pythagorean fuzzy set based topsis method. Int J Uncertain Quantif 7(5)
Garg H (2018) New exponential operational laws and their aggregation operators for interval-valued pythagorean fuzzy multicriteria decision-making. Int J Intell Syst 33(3):653–683
Gou X, Xu Z, Ren P (2016) The properties of continuous pythagorean fuzzy information. Int J Intell Syst 31(5):401–424
Han Y, Deng Y (2019) A novel matrix game with payoffs of Maxitive Belief Structure. Int J Intell Syst 34 (4):690–706
Han Y, Deng Y, Cao Z, Lin CT (2019) An interval-valued pythagorean prioritized operator based game theoretical framework with its applications in multicriteria group decision making. Neural Comput Applic. https://doi.org/10.1007/s00521-019-04014-1
Hwang C, Yoon K (1981) Multiple attribute decision making methods and applications: a state-of-the-art survey. Springer, Berlin
Jiang L, Liao H (2019) Mixed fuzzy least absolute regression analysis with quantitative and probabilistic linguistic information. Fuzzy Sets Syst. https://doi.org/10.1016/j.fss.2019.03.004. http://www.sciencedirect.com/science/article/pii/S0165011419301691
Kang B, Deng Y, Hewage K, Sadiq R (2019) A method of measuring uncertainty for Z-number. IEEE Trans Fuzzy Syst 27(4):731–738
Kang B, Zhang P, Gao Z, Chhipi-Shrestha G, Hewage K, Sadiq R (2019) Environmental assessment under uncertainty using dempster–shafer theory and z-numbers. J Ambient Intell Humaniz Comput pp. Published online, https://doi.org/10.1007/s12,652--019--01,228--y
Khan MSA, Abdullah S (2018) Interval-valued pythagorean fuzzy gra method for multiple-attribute decision making with incomplete weight information. Int J Intell Syst 33(8):1689–1716
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
Li Y, Deng Y (2018) Generalized ordered propositions fusion based on belief entropy. Int J Comput Commun Control 13(5):792–807
Li Y, Deng Y (2019) TDBF: two dimension belief function. Int J Intell Syst 34. https://doi.org/10.1002/int.22,135
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
Liang W, Zhang X, Liu M (2015) The maximizing deviation method based on interval-valued pythagorean fuzzy weighted aggregating operator for multiple criteria group decision analysis. Discret Dyn Nat Soc 2015
Liao H, Jiang L, Lev B, Fujita H (2019) Novel operations of pltss based on the disparity degrees of linguistic terms and their use in designing the probabilistic linguistic electre iii method. Appl Soft Comput 80:450–464
Liao H, Mi X, Yu Q, Luo L (2019) Hospital performance evaluation by a hesitant fuzzy linguistic best worst method with inconsistency repairing. J Clean Prod 232:657–671
Liao H, Qin R, Gao C, Wu X, Hafezalkotob A, Herrera F (2019) Score-hedlisf: a score function of hesitant fuzzy linguistic term set based on hesitant degrees and linguistic scale functions: an application to unbalanced hesitant fuzzy linguistic multimoora. Inform Fusion 48:39–54
Liao H, Wu X (2019) Dnma: a double normalization-based multiple aggregation method for multi-expert multi-criteria decision making. Omega. https://doi.org/10.1016/j.omega.2019.04.001. http://www.sciencedirect.com/science/article/pii/S0305048318302287
Lin J, Zhang Q (2017) Note on continuous interval-valued intuitionistic fuzzy aggregation operator. App Math Model 43(Supplement C):670–677
Liu C, Tang G, Liu P (2017) An approach to multicriteria group decision-making with unknown weight information based on pythagorean fuzzy uncertain linguistic aggregation operators. Math Probl Eng
Liu T, Deng Y, Chan F (2018) Evidential supplier selection based on dematel and game theory. Int J Fuzzy Syst 20(4):1321–1333
Lu M, Wei G, Alsaadi FE, Hayat T, Alsaedi A (2017) Hesitant pythagorean fuzzy hamacher aggregation operators and their application to multiple attribute decision making. J Intell Fuzzy Syst 33(2):1105–1117
Ma Z, Xu Z (2016) Symmetric pythagorean fuzzy weighted geometric/averaging operators and their application in multicriteria decision-making problems. Int J Intell Syst 31(12):1198– 1219
Mi X, Liao H (2019) An integrated approach to multiple criteria decision making based on the average solution and normalized weights of criteria deduced by the hesitant fuzzy best worst method. Comput Ind Eng 133:83–94
Peng X, Yang Y (2015) Some results for pythagorean fuzzy sets. Int J Intell Syst 30(11):1133–1160
Peng X, Yang Y (2016) Fundamental properties of interval-valued pythagorean fuzzy aggregation operators. Int J Intell Syst 31(5):444–487
Peng X, Yuan H (2016) Fundamental properties of pythagorean fuzzy aggregation operators. Fundamenta Informaticae 147(4): 415–446
Peng X, Yuan H, Yang Y (2017) Pythagorean fuzzy information measures and their applications. Int J Intell Syst 32(10):991– 1029
Ren P, Xu Z, Gou X (2016) Pythagorean fuzzy todim approach to multi-criteria decision making. Appl Soft Comput 42:246–259
Singh P, Agrawal R (2018) A customer centric best connected channel model for heterogeneous and IoT networks. J Organ End User Comput (JOEUC) 30(4):32–50
Song Y, Deng Y (2019) A new method to measure the divergence in evidential sensor data fusion. Int J Distrib Sens Netw 15(4), https://doi.org/10.1177/1550147719841,295
Sun R, Deng Y (2019) A new method to identify incomplete frame of discernment in evidence theory. IEEE Access 7(1):15,547–15,555
Sun R, Deng Y (2019) A new method to determine generalized basic probability assignment in the open world. IEEE Access 7(1):52,827–52,835
Wei G, Lu M, Tang X, Wei Y (2018) Pythagorean hesitant fuzzy hamacher aggregation operators and their application to multiple attribute decision making. Int J Intell Syst 33(6):1197–1233
Wei G, Wei Y (2018) Similarity measures of pythagorean fuzzy sets based on the cosine function and their applications. Int J Intell Syst 33(3):634–652
Wu SJ, Wei GW (2017) Pythagorean fuzzy hamacher aggregation operators and their application to multiple attribute decision making. Int J Knowledge-based Int Eng Syst 21(3):189–201
Wu X, Liao H (2019) A consensus-based probabilistic linguistic gained and lost dominance score method. Eur J Oper Res 272(3):1017–1027
Xiao F (2018) A hybrid fuzzy soft sets decision making method in medical diagnosis. IEEE Access 6:25,300–25,312
Xiao F (2019) Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy. Inform Fusion 46(2019):23–32
Xiao F (2019) A multiple criteria decision-making method based on D numbers and belief entropy. Int J Fuzzy Syst. https://doi.org/10.1007/s40,815--019--00,620--2
Xiao F, Ding W (2019) Divergence measure of pythagorean fuzzy sets and its application in medical diagnosis. Appl Soft Comput 79:254–267
Xu H, Deng Y (2019) Dependent evidence combination based on DEMATEL method 34(7):1555–1571
Xu Q, Yu K, Zeng S, Liu J (2017) Pythagorean fuzzy induced generalized owa operator and its application to multi-attribute group decision making. Int J Innov Comput Inform Control 13(5):1527–1536
Yager RR (2004) Owa aggregation over a continuous interval argument with applications to decision making. IEEE Trans Syst Man Cybern B Cybern 34(5):1952–1963
Yager RR (2014) Pythagorean membership grades in multicriteria decision making. IEEE Trans Fuzzy Syst 22(4):958–965
Yager RR (2019) Generalized dempster–shafer structures. IEEE Trans Fuzzy Syst 27(3):428–435
Yager RR, Abbasov AM (2013) Pythagorean membership grades, complex numbers, and decision making. Int J Intell Syst 28(5):436–452
Yang Y, Ding H, Chen ZS, Li YL (2016) A note on extension of topsis to multiple criteria decision making with pythagorean fuzzy sets. Int J Intell Syst 31(1):68–72
Zadeh LA (1996) Fuzzy sets. In: Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by lotfi a zadeh, pp. 394–432. World Scientific
Zeng S (2017) Pythagorean fuzzy multiattribute group decision making with probabilistic information and owa approach. Int J Intell Syst 32(11):1136–1150
Zeng S, Chen J, Li X (2016) A hybrid method for pythagorean fuzzy multiple-criteria decision making. Int J Inf Technol Decis Mak 15(02):403–422
Zhang C, Li D, Ren R (2016) Pythagorean fuzzy multigranulation rough set over two universes and its applications in merger and acquisition. Int J Intell Syst 31(9):921–943
Zhang H, Deng Y (2018) Engine fault diagnosis based on sensor data fusion considering information quality and evidence theory. Adv Mech Eng 10(11):1687814018809,184
Zhang R, Wang J, Zhu X, Xia M, Yu M (2017) Some generalized pythagorean fuzzy bonferroni mean aggregation operators with their application to multiattribute group decision-making. Complexity 2017
Zhang X (2016) Multicriteria pythagorean fuzzy decision analysis: a hierarchical qualiflex approach with the closeness index-based ranking methods. Inf Sci 330:104–124
Zhang X (2016) A novel approach based on similarity measure for pythagorean fuzzy multiple criteria group decision making. Int J Intell Syst 31(6):593–611
Zhang X, Xu Z (2014) Extension of topsis to multiple criteria decision making with pythagorean fuzzy sets. Int J Intell Syst 29(12):1061–1078
Zhao J, Deng Y (2019) Performer selection in human reliability analysis: D numbers approach. Int J Comput Commun Control 14(3):437–452
Zhou L, Tao Z, Chen H, Liu J (2014) Continuous interval-valued intuitionistic fuzzy aggregation operators and their applications to group decision making. Appl Math Model 38(7):2190–2205
Zhou X, Hu Y, Deng Y, Chan FTS, Ishizaka A (2018) A DEMATEL-based completion method for incomplete pairwise comparison matrix in AHP. Ann Oper Res 271(2):1045–1066
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The authors greatly appreciate the reviews’ valuable comments and the editor’s encouragement. The work is partially supported by National Natural Science Foundation of China (Grant Nos. 61573290, 61503237).
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Appendices
Appendix A: Proof of Theorem 1
Proof
Let
A coordinate system is established and shown in Fig. 8, which takes μ as the vertical axis with ν as the horizontal axis. According to the constraints, we know that \(P(\mu _{\beta _{1}} ,\nu _{\beta _{1}})\) and \(P(\mu _{\beta _{2}} ,\nu _{\beta _{2}})\) are the two points of the shaded part in the coordinate system. Based on (41), dTemp(β1, β2) is the distance between point β1 and β2. The maximum and minimum values between them are 0 and \(\sqrt {2}\), respectively. That is, \(d_{Temp}(A, B) \in [0, \sqrt {2}]\). Based on (2), we have 0 ≤ d(β1, β2) ≤ 1. □
Appendix B: Proof of Theorem 4
Proof
As β1 ≤ β2 ≤ β3, we have \(\mu _{\beta _{1}} \leq \mu _{\beta _{2}} \leq \mu _{\beta _{3}}\) and \(\nu _{\beta _{1}} \geq \nu _{\beta _{2}} \geq \nu _{\beta _{3}}\) based on the Definition in [62]. As shown in Fig. 8, we take arbitrary values \(\mu _{\beta _{1}}\), \(\mu _{\beta _{2}}\), \(\mu _{\beta _{3}}\) and \(\nu _{\beta _{1}}\), \(\nu _{\beta _{2}}\), \(\nu _{\beta _{3}}\) on the coordinate axis. Their intersection points β1, β2 and β3 form a triangle. Obviously, ∠β1β2β3 is always an obtuse angle, so β1β2 < β1β3 and β2β3 < β1β3. That is, dTemp(β1, β2) ≤ dTemp(β1, β3) and dTemp(β2, β3) ≤ dTemp(β1, β3). So d(β1, β2) ≤ d(β1, β3) and d(β2, β3) ≤ d(β1, β3). □
Appendix C: Proof of Theorem 6
Proof
According to Definition 4, we have
Since
Since β1 ≤ β2, we have \(0\leq \mu _{\beta _{1}}\leq \mu _{\beta _{2}} \leq 1\) and \(1 \geq \nu _{\beta _{1}} \geq \nu _{\beta _{2}} \geq 0\) according to Definition in [62]. So, \(\mu _{\beta _{1}}+\mu _{\beta _{2}} \geq 0\), \(\mu _{\beta _{1}}-\mu _{\beta _{2}} \leq 0\), \(\nu _{\beta _{1}}+\nu _{\beta _{2}}-2 \leq 0\), \(\nu _{\beta _{1}}-\nu _{\beta _{2}} \geq 0\); that is, \((\mu _{\beta _{1}}+\mu _{\beta _{2}})(\mu _{\beta _{1}}-\mu _{\beta _{2}}) \leq 0\) and \((\nu _{\beta _{1}}+\nu _{\beta _{2}}-2)(\nu _{\beta _{1}}-\nu _{\beta _{2}}) \leq 0\), so \((\mu _{\beta _{1}}^{2}+(\nu _{\beta _{1}}-1)^{2})-(\mu _{\beta _{2}}^{2}+(\nu _{\beta _{2}}-1)^{2}) \leq 0\), then \((\mu _{\beta _{1}}^{2}+(\nu _{\beta _{1}}-1)^{2}) \leq (\mu _{\beta _{2}}^{2}+(\nu _{\beta _{2}}-1)^{2})\), then \(\sqrt {\mu _{\beta _{1}}^{2}+(\nu _{\beta _{1}}-1)^{2}} \leq \sqrt {\mu _{\beta _{2}}^{2}+(\nu _{\beta _{2}}-1)^{2}}\). Similarly, we have \(\sqrt {(\mu _{\beta _{2}}-1)^{2}+\nu _{\beta _{2}}^{2}} \leq \sqrt {(\mu _{\beta _{1}}-1)^{2}+\nu _{\beta _{1}}^{2}}\). So, in (37), we have
and
So, R(β1) −R(β2) ≤ 0, that is, R(β1) ≤R(β2). □
Appendix D: Proof of Theorem 7
Proof
Let
According to the proof of Theorem 1, it is easy to have \(d_{Temp}^{L}(\beta _{1}, \beta _{2}) \in [0, \sqrt 2]\) and \(d_{Temp}^{U}(\beta _{1}, \beta _{2}) \in [0, \sqrt 2]\), that is, \(0 \leq d(\widetilde {\beta }_{1}, \widetilde {\beta }_{2}) \leq 1\) based on (11). □
Appendix E: Proof of Theorem 12
Proof
According to (39) and corresponding definition in [72], we have
so,
so,
and,
so, \({\boldsymbol {\Re }}(\widetilde {\beta }_{1}) - {\boldsymbol {\Re }}(\widetilde {\beta }_{2}) \leq 0\), that is \({\boldsymbol {\Re }}(\widetilde {\beta }_{1}) \leq {\boldsymbol {\Re }}(\widetilde {\beta }_{2})\). □
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Fei, L., Deng, Y. Multi-criteria decision making in Pythagorean fuzzy environment. Appl Intell 50, 537–561 (2020). https://doi.org/10.1007/s10489-019-01532-2
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DOI: https://doi.org/10.1007/s10489-019-01532-2