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Mathematical Biosciences and Engineering

2020, Volume 17, Issue 5: 5604-5617. doi: 10.3934/mbe.2020301
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TOPSIS-based entropy measure for intuitionistic trapezoidal fuzzy sets and application to multi-attribute decision making

  • 1.
    School of Economics and Management, East China JiaoTong University, Nanchang 330013, China
  • 2.
    College of Modern Economics & Management, Jiangxi University of Finance and Economics, Nanchang 330013, China
  • 3.
    Collaborative Innovation center, Jiangxi University of Finance and Economics, Nanchang 330013, China
  • Received: 08 May 2020 Accepted: 31 July 2020 Published: 18 August 2020

  • As an extension of intuitionistic fuzzy numbers, intuitionistic trapezoidal fuzzy numbers (ITrFNs) are useful in expressing complex fuzzy information with an 'interval value'. This study focuses on multi-attribute decision-making (MADM) problems with unknown attribute weights under an ITrFN environment. We initially present an entropy measure for ITrFNs by using the relative closeness of technique for order preference by similarity to an ideal solution. From the view of the reliability and certainty of decision data, we present an approach to determine the attribute weights. Subsequently, a new method to solve intuitionistic trapezoidal fuzzy MADM problems with unknown attribute weight information is proposed. A numerical example is provided to verify the practicality and effectiveness of the proposed method.

    Citation: Yefu Zheng, Jun Xu, Hongzhang Chen. TOPSIS-based entropy measure for intuitionistic trapezoidal fuzzy sets and application to multi-attribute decision making[J]. Mathematical Biosciences and Engineering, 2020, 17(5): 5604-5617. doi: 10.3934/mbe.2020301

    Related Papers:

  • As an extension of intuitionistic fuzzy numbers, intuitionistic trapezoidal fuzzy numbers (ITrFNs) are useful in expressing complex fuzzy information with an 'interval value'. This study focuses on multi-attribute decision-making (MADM) problems with unknown attribute weights under an ITrFN environment. We initially present an entropy measure for ITrFNs by using the relative closeness of technique for order preference by similarity to an ideal solution. From the view of the reliability and certainty of decision data, we present an approach to determine the attribute weights. Subsequently, a new method to solve intuitionistic trapezoidal fuzzy MADM problems with unknown attribute weight information is proposed. A numerical example is provided to verify the practicality and effectiveness of the proposed method.


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