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Vector similarity measures of hesitant fuzzy linguistic term sets and their applications.

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Updated: Feb 1, 2026

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Deriving the priority weights from probabilistic linguistic preference relation with unknown probabilities.

Yongming Song1

  • 1School of Business Administration, Shandong Technology and Business University, Yantai, China.

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Summary
This summary is machine-generated.

This study introduces a probabilistic linguistic preference relation (PLPR) model to derive alternative weights from decision-making data. It addresses challenges in assigning probabilities, enhancing decision analysis accuracy.

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Area of Science:

  • Decision Sciences
  • Operations Research
  • Fuzzy Logic

Background:

  • Probabilistic linguistic term sets (PLTS) offer superior descriptive power compared to hesitant fuzzy sets.
  • Probabilistic linguistic preference relations (PLPR) are valuable for complex decision-making but face challenges in probability assignment.

Purpose of the Study:

  • To define expected consistency for PLPR and develop a model for deriving occurrence probabilities.
  • To introduce a consistency-improving algorithm for evaluating and adjusting PLPR acceptability.
  • To demonstrate the method's effectiveness in real-world scenarios, such as employment city selection.

Main Methods:

  • A probability computing model is established to derive occurrence probabilities from PLPR using priority weights.
  • A consistency-improving iterative algorithm is presented to assess and enhance PLPR consistency.
  • The algorithm determines satisfaction consistency levels for unacceptable PLPR.

Main Results:

  • The proposed model successfully derives probabilities of occurrence for PLPR, incorporating priority weights.
  • The iterative algorithm effectively improves PLPR consistency to acceptable levels.
  • The method demonstrates practical utility in a real-world employment-city selection case.

Conclusions:

  • The developed method provides a robust approach for deriving priority weights from PLPR, overcoming probability assignment difficulties.
  • The consistency-improving algorithm ensures the reliability and acceptability of the preference relations.
  • This research offers a valuable tool for complex decision-making problems involving probabilistic linguistic information.