Advanced TOPSIS-Based College Selection MCGDM Problem in Trapezoidal Pythagorean Fuzzy Environment

Avishek Chakraborty, Tipu Sultan Haque, Rasel Mondal and Shariful Alam ()
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Avishek Chakraborty: Academy of Technology, Adisaptagram, Department of Engineering Science
Tipu Sultan Haque: Indian Institute of Engineering Science and Technology, Department of Mathematics
Rasel Mondal: Indian Institute of Engineering Science and Technology, Department of Mathematics
Shariful Alam: Indian Institute of Engineering Science and Technology, Department of Mathematics

Chapter Chapter 16 in Fuzzy Optimization, Decision-making and Operations Research, 2023, pp 343-376 from Springer

Abstract: Abstract Technique for order preference by similarity to ideal solution (TOPSIS) is a celebrated technique to solve the decision-making problems. Pythagorean fuzzy set is a generalization of intuitionistic fuzzy set that can deal with uncertain and incomplete information more appropriately. The motivation of the study is to develop an improved TOPSIS technique in trapezoidal Pythagorean fuzzy environment that can handle real-life decision-making problems in more effective manner and robust way. In this chapter, we have defined trapezoidal Pythagorean fuzzy numbers and discussed their various algebraic properties. Then, new distance functions have been defined for the trapezoidal Pythagorean fuzzy environment that is used to calculate relative closeness coefficient. Furthermore, we have introduced an aggregation operator namely trapezoidal Pythagorean fuzzy weighted arithmetic operator, which is used to develop an improved TOPSIS technique based on new distance functions under the trapezoidal Pythagorean fuzzy environment. The newly developed TOPSIS technique has been demonstrated and applied it to select best institute/college. Finally, the comparative analysis and sensitivity analysis are given to discuss the advantages and reliability of the proposed technique.

Keywords: TrPyFN; Aggregation operators; TOPSIS; MCGDM (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-031-35668-1_16

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