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Multiple Attribute Group Decision Analysis for Intuitionistic Triangular and Trapezoidal Fuzzy Numbers

Multiple Attribute Group Decision Analysis for Intuitionistic Triangular and Trapezoidal Fuzzy Numbers

John Robinson P.
Copyright: © 2016 |Volume: 5 |Issue: 3 |Pages: 35
ISSN: 2156-177X|EISSN: 2156-1761|EISBN13: 9781466693227|DOI: 10.4018/IJFSA.2016070104
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MLA

John Robinson P. "Multiple Attribute Group Decision Analysis for Intuitionistic Triangular and Trapezoidal Fuzzy Numbers." IJFSA vol.5, no.3 2016: pp.42-76. http://doi.org/10.4018/IJFSA.2016070104

APA

John Robinson P. (2016). Multiple Attribute Group Decision Analysis for Intuitionistic Triangular and Trapezoidal Fuzzy Numbers. International Journal of Fuzzy System Applications (IJFSA), 5(3), 42-76. http://doi.org/10.4018/IJFSA.2016070104

Chicago

John Robinson P. "Multiple Attribute Group Decision Analysis for Intuitionistic Triangular and Trapezoidal Fuzzy Numbers," International Journal of Fuzzy System Applications (IJFSA) 5, no.3: 42-76. http://doi.org/10.4018/IJFSA.2016070104

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Abstract

Solving Multiple Attribute Group Decision Making (MAGDM) problems has become one of the most important researches in recent days. In situations where the information or the data is of the form of an Intuitionistic Triangular Fuzzy Number (ITrFN) or Intuitionistic Trapezoidal Fuzzy Number (ITzFN), a new distance function is defined for ranking the alternatives in the decision making process. After processing the decision information through a sequence of arithmetic aggregation operators, namely, the Intuitionistic Triangular Fuzzy Weighted Arithmetic Averaging (ITrFWAA), Intuitionistic Triangular Fuzzy Ordered Weighted Averaging (ITrFOWA) operator and the Intuitionistic Triangular Fuzzy Hybrid Aggregation (ITrFHA) operator, the proposed distance function is utilized to rank the best alternative. A model is proposed to solve MAGDM problems using the developed distance formula defined for ITrFNs. Numerical illustration is provided and comparisons are made with some of the existing MAGDM models and ranking procedures.

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