AI Article Synopsis

  • It can be challenging to assess the importance of different factors when evaluating alternatives, especially when precise membership grades are hard to define in fuzzy sets.
  • To address this, the article introduces "interval type-2 trapezoidal fuzzy preference relations" (IT2TrFPRs) to provide a more powerful way to express preferences without needing specific priority weights.
  • A decision-making process is outlined using hybrid averages and a least deviation model to rank alternatives based on their distance from an ideal solution, illustrated through a real-life example of selecting a location.

Article Abstract

In real life situation, it is often difficult to judge the relative importance of different parameters being considered for evaluating some alternatives. In the context of fuzzy sets, it is a situation where it is difficult to define precise membership grades for attribute values. Here we require more generalized type of fuzzy sets which have a greater representational power than ordinary fuzzy sets. For this purpose we use "interval type-2 trapezoidal fuzzy preference relations (IT2TrFPRs)" in this article as a generalization of fuzzy preference relations and consider the environment discussed above, where there is no information on priority weights. A collective decision matrix will be constructed on the basis of hybrid averages using weighted averaging and signed distance based OWA operation. Then a least deviation model will be employed in order to determine the priority weight vectors. Finally, the alternatives will be ranked on the basis of weighted normalized signed distance of each alternative from the ideal solution. Moreover, a real life example of location selection is illustrated to elaborate the effectiveness of the proposed scheme.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11320439PMC
http://dx.doi.org/10.1016/j.heliyon.2024.e34422DOI Listing

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