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Fuzzy incomplete linguistic preference relations.

Tien-Chin Wang1, Shu-Li Huang1

  • 1Department of International Business, National Kaohsiung University of Science and Technology, 415 Jian Gong Rd., Kaohsiung City, 80778 Taiwan.

Soft Computing
|December 21, 2022
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Summary
This summary is machine-generated.

This study introduces the fuzzy incomplete linguistic preference relations (Fuzzy InLinPreRa) approach for complex decision-making. Fuzzy InLinPreRa enhances solution accuracy by measuring decision-maker consistency and incorporating objective expert and criteria weights.

Keywords:
Fuzzy InLinPreRaFuzzy incomplete linguistic preference relationsFuzzy preference relationsMulti-criteria decision-making

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Area of Science:

  • Decision Sciences
  • Fuzzy Logic Systems
  • Operations Research

Background:

  • Preference relations are crucial for modeling decision-making processes.
  • Existing methods struggle with complex, uncertain economic, social, and managerial problems.
  • There is a need for robust approaches to handle incomplete and fuzzy information in decision-making.

Purpose of the Study:

  • To introduce and evaluate the Fuzzy InLinPreRa approach for decision-making problems.
  • To enhance the accuracy and reasonableness of solutions in complex decision scenarios.
  • To demonstrate the method's feasibility through an empirical example.

Main Methods:

  • Development of the fuzzy incomplete linguistic preference relations (Fuzzy InLinPreRa) approach.
  • Utilizing consistency measurements of decision-makers' evaluations.
  • Incorporating objective weights for criteria and experts.

Main Results:

  • The Fuzzy InLinPreRa approach provides more accurate and reasonable solutions.
  • Consistency measurements lead to improved decision outcomes.
  • The method effectively handles increasing complexity and uncertainty in decision problems.

Conclusions:

  • Fuzzy InLinPreRa is a feasible and effective method for decision-making under uncertainty.
  • The approach allows for objective consideration of criteria and expert weights.
  • This method offers a valuable tool for addressing complex economic, social, and managerial challenges.