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TOPSIS-based consensus model for group decision-making with incomplete interval fuzzy preference relations.

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    This study introduces a consensus model for group decision-making (GDM) using incomplete interval fuzzy preference relations. It addresses vagueness and subjectivity by estimating missing data and aggregating expert judgments for better partnership selection.

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    Area of Science:

    • Decision Sciences
    • Fuzzy Logic Systems
    • Operations Research

    Background:

    • Real-world decisions often involve vague information and subjective expert judgments.
    • Incomplete interval fuzzy preference relations are commonly used to represent such uncertain preferences.
    • Existing group decision-making (GDM) methods struggle with incomplete and uncertain preference data.

    Purpose of the Study:

    • To develop a robust consensus model for GDM with incomplete interval fuzzy preference relations.
    • To enhance the accuracy of decision-making by effectively handling missing preference data.
    • To improve the process of partnership selection in virtual enterprise formation.

    Main Methods:

    • Defined a novel consistency measure for incomplete interval fuzzy preference relations.
    • Utilized a goal programming model to estimate missing interval preference values based on consistency.
    • Constructed an ideal interval fuzzy preference relation using an induced ordered weighted averaging operator.
    • Developed a similarity degree to aggregate expert preferences, prioritizing those with higher similarity.

    Main Results:

    • The proposed model effectively estimates missing preference values while maintaining consistency.
    • The consensus model successfully aggregates incomplete interval fuzzy preference relations.
    • The method demonstrated practical applicability in partnership selection for virtual enterprises.

    Conclusions:

    • The developed consensus model provides a reliable framework for GDM with incomplete interval fuzzy preference relations.
    • The approach enhances decision accuracy by systematically addressing data uncertainty and subjectivity.
    • This methodology offers a valuable tool for complex decision problems, particularly in virtual enterprise formation.