Fuzzy Filtering Ranking Method for Multi-criteria Decision Making

Cheng-Yuan Ku, Ting-Yu Chang

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Ranking methods are widely used in the fields of social science and economics, e.g., for national competitiveness, university academic, and corporate competitiveness rankings, and they are vital in the field of multi-criteria decision making. Well-known ranking methods include TOPSIS, VIKOR, PROMETHE, and AHP. However, these methods exhibit high complexity and lack of flexibility, which hinder their application and promotion in solving practical ranking issues. To make the ranking method simpler and more suitable for use in human decision making, we propose a novel ranking method called fuzzy filtering ranking, which combines the concepts of the Likert scale and discrete fuzzy score to resolve the above-mentioned problem. To demonstrate the effectiveness of the proposed method, a program using Excel was designed to perform 20,000 random ranking experiments and compare the ranking results of TOPSIS, VIKOR, and the proposed FFR. We found that in the comparison between the proposed method and TOPSIS, 84.1% (data in uniform distribution) and 82.3% (data in normal distribution) of the ranking results were the same, and in the comparison between the proposed method and VIKOR, 83.7% (data in uniform distribution) and 83.7% (data in normal distribution) of the ranking results were the same. Finally, the simplicity and reliability of the proposed method were demonstrated through experiments.

    Original languageAmerican English
    Article number107217
    JournalComputers and Industrial Engineering
    Volume156
    DOIs
    StatePublished - Jun 2021

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