JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2023, Vol. 58 ›› Issue (6): 9-17.doi: 10.6040/j.issn.1671-9352.0.2022.260

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Probabilistic q-rung hesitant fuzzy TODIM method and its application

Yu ZHOU1,2,3(),Ligang ZHOU1,2,3,*(),Zhichao LIN2,3,Xin XU2,3   

  1. 1. School of Big Data and Statistics, Anhui University, Hefei 230601, Anhui, China
    2. School of Mathematical Sciences, AnhuiUniversity, Hefei 230601, Anhui, China
    3. Anhui Center for Applied Mathematics, Anhui University, Hefei 230601, Anhui, China
  • Received:2022-04-09 Online:2023-06-20 Published:2023-05-23
  • Contact: Ligang ZHOU E-mail:1439552596@qq.com;shuiqiaozlg@126.com

Abstract:

To measure differences in probabilistic q-rung hesitant fuzzy information, a novel measure of the probabilistic q-rung hesitant fuzzy distance is proposed based on the score function and overall hesitancy. The properties of this measure are discussed. A probabilistic q-rung hesitant fuzzy TODIM method is proposed based on the new distance measure. Finally, a practical example is presented to illustrate the reasonableness and effectiveness of the proposed method, and a sensitivity analysis is performed.

Key words: probabilistic q-rung hesitant fuzzy set, distance measure, TODIM, multi-attribute decision-making

CLC Number: 

  • O159

Table 1

Probabilistic q-rung hesitant fuzzy decision-making matrix$\widetilde{\boldsymbol{R}}=\left(\tilde{r}_{i j}\right)_{5 \times 4}$"

C1 C2 C3 C4
X1 {〈0.5, 0.2〉(0.6),
〈0.6, 0.3〉(0.4)}
{〈0.4, 0.3〉(1)} {〈0.2, 0.5〉(0.3),
〈0.3, 0.6〉(0.7)}
{〈0.6, 0.1〉(0.5),
〈0.7, 0.2〉(0.5)}
X2 {〈0.3, 0.3〉(0.3),
〈0.5, 0.3〉(0.4),
〈0.5, 0.4〉(0.3)}
{〈0.5, 0.1〉(0.3),
〈0.6, 0.2〉(0.5),
〈0.7, 0.1〉(0.2)}
{〈0.4, 0.2〉(0.5),
〈0.5, 0.2〉(0.3),
〈0.6, 0.3〉(0.2)}
{〈0.5, 0.2〉(0.6)
〈0.7, 0.2〉(0.4)}
X3 {〈0.3, 0.2〉(0.4),
〈0.7, 0.2〉(0.6)}
{〈0.6, 0.2〉(0.5),
〈0.7, 0.2〉(0.5)}
{〈0.1, 0.3〉(0.2),
〈0.2, 0.5〉(0.2),
〈0.3, 0.6〉(0.6)}
{〈0.2, 0.4〉(0.6),
〈0.3, 0.6〉(0.4)}
X4 {〈0.3, 0.2〉(0.4),
〈0.4, 0.3〉(0.3),
〈0.5, 0.3〉(0.3)}
{〈0.4, 0.1〉(0.6),
〈0.6, 0.3〉(0.4)}
{〈0.3, 0.3〉(0.4),
〈0.4, 0.4〉(0.6)}
{〈0.5, 0.1〉(0.6),
〈0.6, 0.2〉(0.4)}
X5 {〈0.3, 0.1〉(0.7),
〈0.5, 0.2〉(0.3)}
{〈0.4, 0.1〉(0.6),
〈0.6, 0.3〉(0.4)}
{〈0.3, 0.5〉(0.4),
〈0.4, 0.2〉(0.2),
〈0.5, 0.2〉(0.4)}
{〈0.5, 0.2〉(1)}

Table 2

Normalized decision-making matrix R =(rij)5×4"

C1 C2 C3 C4
X1 {〈0.5, 0.2〉(0.6),
〈0.5, 0.2〉(0),
〈0.6, 0.3〉(0.4)}
{〈0.4, 0.3〉(0),
〈0.4, 0.3〉(0),
〈0.4, 0.3〉(1)}
{〈0.3, 0.6〉(0.7),
〈0.2, 0.5〉(0),
〈0.2, 0.5〉(0.3)}
{〈0.6, 0.1〉(0.5),
〈0.6, 0.1〉(0),
〈0.7, 0.2〉(0.5)}
X2 {〈0.3, 0.3〉(0.3),
〈0.5, 0.4〉(0.3),
〈0.5, 0.3〉(0.4)}
{〈0.5, 0.1〉(0.3),
〈0.6, 0.2〉(0.5),
〈0.7, 0.1〉(0.2)}
{〈0.4, 0.2〉(0.5),
〈0.5, 0.2〉(0.3),
〈0.6, 0.3〉(0.2)}
{〈0.5, 0.2〉(0.6)
〈0.5, 0.2〉(0),
〈0.7, 0.2〉(0.4)}
X3 {〈0.3, 0.2〉(0.4),
〈0.3, 0.2〉(0),
〈0.7, 0.2〉(0.6)}
{〈0.6, 0.2〉(0.5),
〈0.6, 0.2〉(0),
〈0.7, 0.2〉(0.5)}
{〈0.3, 0.6〉(0.6),
〈0.2, 0.5〉(0.2),
〈0.1, 0.3〉(0.2)}
{〈0.3, 0.6〉(0.4),
〈0.2, 0.4〉(0),
〈0.2, 0.4〉(0.6)}
X4 {〈0.3, 0.2〉(0.4),
〈0.4, 0.3〉(0.3),
〈0.5, 0.3〉(0.3)}
{〈0.4, 0.1〉(0.6),
〈0.4, 0.1〉(0),
〈0.6, 0.3〉(0.4)}
{〈0.3, 0.3〉(0.4),
〈0.3, 0.3〉(0),
〈0.4, 0.4〉(0.6)}
{〈0.5, 0.1〉(0.6),
〈0.5, 0.1〉(0),
〈0.6, 0.2〉(0.4)}
X5 {〈0.3, 0.1〉(0.7),
〈0.3, 0.1〉(0),
〈0.5, 0.2〉(0.3)}
{〈0.4, 0.1〉(0.6),
〈0.4, 0.1〉(0),
〈0.6, 0.3〉(0.4)}
{〈0.3, 0.5〉(0.4),
〈0.4, 0.2〉(0.2),
〈0.5, 0.2〉(0.4)}
{〈0.5, 0.2〉(0),
〈0.5, 0.2〉(0),
〈0.5, 0.2〉(1)}

Fig.1

Plot of ξi for different values of parameterq"

Fig.2

Plot of ξi for different values of parameter θ"

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