A three-phase method for Pythagorean fuzzy multi-attribute group decision making and application to haze management

Group decision‐making based on pythagorean fuzzy TOPSIS method

“…Liang and Xu explored the novel concept of hesitant PFS (HPFS) to encounter hesitant fuzzy environment and presented hesitant Pythagorean fuzzy TOPSIS (PF‐TOPSIS) by considering hesitant human reasoning in decision‐making. Recently, Wan et al proposed a three‐phase algorithm for addressing MCDM with PFNs in group decision‐making environment and illustrated the example of haze management. Although MCDM has begun to apply and penetrate many new fields of research but with the growing complexity arising in practical decision problems, it may be an arduous process for individual decision‐maker to deal with such situation and to provide satisfactory as well as authentic solution to the problem.…”

“…Hence, the space of Pythagorean fuzzy membership grades is greater than the space of intuitionistic fuzzy membership grades because of the corresponding constraint conditions as shown in Figure 1 and is in general capable to accommodate greater degrees of uncertainty in MCDM problems. 22 31 proposed a three-phase algorithm for addressing MCDM with PFNs in group decision-making environment and illustrated the example of haze management. Although MCDM has begun to apply and penetrate many new fields of research but with the growing complexity arising in practical decision problems, it may be an arduous process for individual decision-maker to deal with such situation and to provide satisfactory as well as authentic solution to the problem.…”

Edas Method for Multiple Criteria Group Decision Making With Picture Fuzzy Information and Its Application to Green Suppliers Selections

“…In previous work, lots of MCDM models such as the VIKOR method (H. C. Liu, L. Liu, & Wu, 2013;Yang, Pang, Shi, & Wang, 2018;Zhou, Wang, & Zhang, 2018), the ELECTRE method (N. Chen & Xu, 2015; J. J. Peng, Wang, & Wu, 2017;Zhang, Wang, & Chen, 2016), the TOPSIS method (Liao, Si, Xu, & Fujita, 2018;Wan, Li, & Dong, 2018;Zeng & Xiao, 2018), the PROMETHEE method (S. Hajlaoui & N. Halouani, 2013;Liao & Xu, 2014;Muirhead, 1902;Sennaroglu, Yilmazer, Tuzkaya, & Tuzkaya, 2018;Y. N. Wu, Wang, Hu, Ke, & Li, 2018), the GRA method, the MULTIMOORA method (Aydin, 2018;Liu, You, Lu, & Shan, 2014;Zhao, You, & Liu, 2017) and the TODIM method (Huang & Wei, 2018;Ji, Zhang, & Wang, 2018;Qin, Liang, Li, Chen, & Yu, 2017;Ren, Xu, Wang, & Ieee, 2017;G.…”

Some interval‐valued q‐rung orthopair weighted averaging operators and their applications to multiple‐attribute decision making

“…IFS and PFS have good use in solving practical multiple-attribute decision making (MADM) problems, and great achievements have been achieved in academia. [5][6][7][8][9][10][11][12][13][14][15][16][17] Recently, Yager 18 further enlarged the space of the belief of the decision makers (DMs) about membership degree and nonmembership degree in PFS, and proposed the definition of q-rung orthopair fuzzy sets (q-ROFS), in which the membership degree μ and the nonmembership degree ν satisfy the condition ≤ μ ν + 1 q q , ≥ q 1. Obviously, the q-ROFS accommodates more uncertainty than the PFSs and the intuitionistic fuzzy set, and its application is more extensive than that of the PFSs and the intuitionistic fuzzy set.…”