An approach towards decision making and shortest path problems using the concepts of interval‐valued Pythagorean fuzzy information

2019 ◽  
Vol 34 (10) ◽  
pp. 2403-2428 ◽  
Author(s):  
Naeem Jan ◽  
Muhammad Aslam ◽  
Kifayat Ullah ◽  
Tahir Mahmood ◽  
Jun Wang
Keyword(s):  
Decision Making ◽  
Shortest Path ◽  
Fuzzy Information ◽  
Shortest Path Problems ◽  
Interval Valued

An Approach Towards Decision-Making and Shortest Path Problems Based on T-Spherical Fuzzy Information

2020 ◽  
Vol 22 (5) ◽  
pp. 1521-1534
Author(s):  
Lemnaouar Zedam ◽  
Naeem Jan ◽  
Ewa Rak ◽  
Tahir Mahmood ◽  
Kifayat Ullah
Keyword(s):  
Decision Making ◽  
Shortest Path ◽  
Fuzzy Information ◽  
Shortest Path Problems

Some Interval-Valued Pythagorean Fuzzy Einstein Weighted Averaging Aggregation Operators and Their Application to Group Decision Making

2018 ◽  
Vol 29 (1) ◽  
pp. 393-408 ◽  
Author(s):  
Khaista Rahman ◽  
Saleem Abdullah ◽  
Muhammad Sajjad Ali Khan
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Aggregation Operator ◽  
Aggregation Operators ◽  
Decision Makers ◽  
Weighted Averaging ◽  
Fuzzy Information ◽  
Multiple Attribute ◽  
Interval Valued

Abstract In this paper, we introduce the notion of Einstein aggregation operators, such as the interval-valued Pythagorean fuzzy Einstein weighted averaging aggregation operator and the interval-valued Pythagorean fuzzy Einstein ordered weighted averaging aggregation operator. We also discuss some desirable properties, such as idempotency, boundedness, commutativity, and monotonicity. The main advantage of using the proposed operators is that these operators give a more complete view of the problem to the decision makers. These operators provide more accurate and precise results as compared the existing method. Finally, we apply these operators to deal with multiple-attribute group decision making under interval-valued Pythagorean fuzzy information. For this, we construct an algorithm for multiple-attribute group decision making. Lastly, we also construct a numerical example for multiple-attribute group decision making.

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