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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

MAGDM linear-programming models with distinct uncertain preference structures.

Zeshui S Xu1, Jian Chen

  • 1Antai School of Economic and Management, Shanghai Jiaotong University, Shanghai 200052, China. xu_zeshui@263.net

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|September 12, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces linear-programming models for multiple-attribute group decision-making (MAGDM) with uncertain preferences. The approach effectively ranks alternatives using interval utility values, fuzzy, and multiplicative preference relations.

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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

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Published on: March 1, 2022

Area of Science:

  • Operations Research
  • Decision Sciences
  • Artificial Intelligence

Background:

  • Group decision-making is crucial but complex, especially with incomplete information.
  • Multiple-attribute group decision-making (MAGDM) requires handling diverse preference structures.
  • Existing methods often struggle with uncertain or incomplete preference data.

Purpose of the Study:

  • To develop novel linear-programming models for MAGDM problems.
  • To address uncertain preference structures including interval utility values, fuzzy, and multiplicative relations.
  • To integrate subjective and objective information for robust decision support.

Main Methods:

  • Formulation of linear-programming models based on decision matrices.
  • Development of models to incorporate three distinct uncertain preference structures.
  • Creation of an integrated approach combining subjective and objective data.
  • Proposal of a straightforward method for ranking and selecting alternatives.

Main Results:

  • Successfully developed and applied linear-programming models for MAGDM with uncertain preferences.
  • Demonstrated the integration of interval utility values, interval fuzzy preference relations, and interval multiplicative preference relations.
  • The proposed approach effectively handles incomplete attribute weights and decision-maker preferences.
  • Validated the models' applicability to traditional preference structures.

Conclusions:

  • The developed linear-programming models provide an effective framework for MAGDM with distinct uncertain preference structures.
  • The integrated approach offers a robust method for decision-making by combining various preference information types.
  • The proposed methodology is practical and applicable to real-world decision problems, as illustrated by a case study.